Robust Image Embedded Watermarking Using DCT and Listless SPIHT

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Article Robust Image Embedded Watermarking Using DCT and Listless SPIHT

J. L. Divya Shivani and Ranjan K. Senapati * Department of ECE, K L University, Vaddeswaram, Guntur dist, Andhra Pradesh 522502, India; [email protected] * Correspondence: [email protected]; Tel.: +91-898-587-7023

Academic Editor: Luis Javier Garcia Villalba Received: 3 June 2017; Accepted: 5 July 2017; Published: 12 July 2017

Abstract: This paper presents a DCT-based (DCT: discrete cosine transform) listless set partitioning in hierarchical trees (SPIHT) digital watermarking technique that is robust against several common attacks such as cropping, ﬁltering, sharpening, noise, inversion, contrast manipulation, and compression. The proposed technique is made further robust by the incorporation of the Chinese remainder theorem (CRT) encryption technique. Our scheme is compared with the recently proposed CRT-based DCT technique, CRT-based spatial domain watermarking, and DCT-based inter block correlation techniques. Extensive simulation experiments show better robustness in common image manipulations and, at the same time, the proposed technique successfully makes the watermark perceptually invisible. A better Tamper Assessment Function (TAF) value of 2–15% and a better Normalized Correlation (NC) is achieved compared to some of the above techniques. In particular, the proposed technique shows better robustness on compression attacks at moderate to higher compression ratios. It is possible to maintain the imperceptibility and low TAF for various values by doubling the capacity of the watermark.

Keywords: discrete cosine transform; watermarking; cryptography; random number generation; normalized correlation

1. Introduction With the rapid growth in technology, the internet has become the most popular channel for every user to download digital multimedia data like images and videos. Since the environment of internet is open, this data can easily be tampered with or manipulated, for which data integrity must be ensured. Digital watermarking is a common technique that used to verify this integrity. The watermarking technique consists of three parts, namely: (i) the watermark; (ii) the embedding stage, and; (iii) the veriﬁcation stage. In the embedding stage the algorithm incorporates the watermark into the host image. However, the algorithm extracts and authenticates the watermark in the veriﬁcation stage. The goal of image authentication is to verify the originality of the host image by detecting malicious manipulations. There are two essential requirements for image watermarking. One is invisibility, i.e., the visual quality of the watermarked images should not be destroyed by embedding the watermark. The other is robustness, i.e., the watermark should be able to resist attacks, even if the attacks are deliberately made [1]. Digital watermarking techniques are performed in the spatial domain [2] and in the transformed domain [3–7]. The transformed domain is more robust and operates more imperceptibility than the spatial domain [8,9]. In the transform domain DCT make use of energy distribution properties and human visual system scheme for embedding the watermark [10]. Blind watermarking schemes with interesting results havebeen proposed in [11,12]. Some of the other transformed-domain watermarking schemes have been proposed in [13–17]. SVD (singular value decomposition) is also

Future Internet 2017, 9, 33; doi:10.3390/fi9030033 www.mdpi.com/journal/futureinternet Future Internet 2017, 9, 33 2 of 16 used in watermarking where the properties of U coefficients (after SVD decomposition) modification cause less degradation of image quality [17–22]. Some of the watermarking techniques using the Ridgelet transform are also proposed [23,24]. The CRT-based scheme, which provides robustness against some common image processing manipulations, is proposed in [25,26]. Digital watermarking is also performed using segmentation of DCT blocks [27]. Each DC coefficient is extracted and stored in a matrix before processing, making it expensive in terms of memory requirement [28]. Hsu and Wu [6] used middle DCT coefficients for embedding watermark. The algorithm is not very robust against JPEG compression. Watermarking schemes can be implemented in VLSI (very large scale integration) chips. Hardware implementation in consumer electronic devices such as digital camera, PDAs, and mobile devices requires low power, high reliability, real-time performance and easy integration. For example, viability of integration of watermarking chips in any digital camera or JPEG codec. Several issues related to hardware implementation of several watermarking algorithms for images and videos have been reported by Kougianos et al. [29]. DCT domain watermarking is popular for hardware implementation because there are a number of fast algorithms exists [30–34]: 8 × 8 DCT is mostly used for hardware implementation; 4 × 4 DCT provides less computation at a cost of less energy compaction. SPIHT [35] is a benchmark algorithm for the compression of still images and videos in an embedded fashion. It has precise rate control, i.e., the user can set any target bit rate (compression ratio) to reconstruct the compressed images. However, SPIHT uses an auxiliary list, which causes a lot of memory management problem for hardware realization. To overcome this listless SPIHT has been proposed which does not make use of auxiliary lists [36]. The state information that keeps track of set partitions is held in a state marker table in the memory. This algorithm takes less memory and is easy to implement in hardware compared to a conventional JPEG. Recently, CRT-based watermarking with some results has been reported in [25,26]. CRT provides additional security, for example, in selecting a set of relatively prime numbers £ = {P1, P2, ... Pr}, a large integer K can be represented by a set of smaller integers {k1, k2 ... . kn}. It is extremely difficult to get back the K value without the knowledge of £. This fact provides additional security. Further, CRT is based on simultaneous congruence of modular arithmetic and computationally efficient. This motivates the authors to combine listless SPIHT with CRT techniques in a blind watermarking scheme to provide added security and robustness against compression attacks. To the best of the authors’ knowledge there is no such work proposed in the literature that combines listless SPIHT with CRT in blind watermarking schemes. The proposed method exhibits superior performance in terms of PSNR (peak signal to noise ratio)/structural similarity (SSIM) and tamper assessment function (TAF) over most of the recently proposed schemes in literature. Good TAF (in %) and Normalized Correlation (NC) values are also obtained for most of the common attacks such as cropping, tampering, noise addition, brightening, sharpening and compression. Attacks such as scaling, rotation, filtering and geometric transformation are not considered in this paper. The rest of the paper is organized as follows: Related works are presented in Section2. Section3 discusses the implementation of embedding and the extraction aspect of the proposed scheme. Comparison results are presented in Section4. Finally, in Section5 the conclusion and future research direction have been made.

2. Related Work SPIHT is a tree-based image compression algorithm. It scans the transformed coefficients in a depth first search. Set partitioning embedded block (SPECK) is block based image compression algorithm [37]. However, it scans the coefficients using a breadth-first approach. The listless variants of SPIHT and SPECK are called Listless SPIHT (i.e., NLS) and Listless SPECK (LSK), respectively [36,38]. These listless algorithms have been proposed to overcome the memory management problems associated with SPIHT and SPECK. Listless SPIHT and LSK can compress images from a lossy to Future Internet 2017, 9, 33 3 of 16 a lossless format. These algorithms generate a bit string in order of importance. That means that the lower part of the bit string contains more information of the image than higher part of the bit Future Internet 2017, 9, 33 3 of 16 string. The user can truncate at any point of the compressed bit string to reconstruct an image, which isthat a compressedthe lower part representationof the bit string contains of the more original information image. of However, the image than none higher of these part of algorithms the bit are used instring. image/video The user can watermarking truncate at any point schemes of the in compressed the literature. bit string Most to reconstruct of the works an image in, literaturewhich use JPEG/JPEGis a compressed 2000 for compressionrepresentation ofof the original watermarked image. Ho images.wever, none Compression of these algorithms using listless are used SPIHT in and image/video watermarking schemes in the literature. Most of the works in literature use JPEG/JPEG LSK has superior performance than JPEG and is highly competitive with JPEG 2000 [38]. JPEG 2000 2000 for compression of the watermarked images. Compression using listless SPIHT and LSK has is computationallysuperior performance intensive than because JPEG and of contextis highly modelling competitive and with adaptive JPEG 2000 arithmetic [35]. JPEG coding. 2000 is Listless SPIHT andcomputation LSK areally much intensive lesscomplex because of and context take lessmodelling memory and for adaptive compression arithmetic and coding. reconstruction. Listless The authorsSPIHT considered and LSK a modifiedare much less listless complex SPIHT and for take compression less memory offorthe compression watermarked and reconstructio image fromn. lossy to lossless,The because authors compression considered a modified is considered listless asSPIHT one for of thecompression attacks inof the watermarked watermarking image scheme from [39,40]. lossy to lossless, because compression is considered as one of the attacks in the watermarking CRT-based encryption technique provides additional security and robustness against the existing scheme [25,26,39,40]. schemes. PatraCRT-based et al. encryption proposed technique DCT-based provides CRT techniquesadditional security that provide and robustness better robustnessagainst the against variousexisting attacks schemes. [25,26]. Patra In order et al. proposed to achieve DCT the-based merit CRT of techniques both CRT that and provide listless better SPIHT, robustness we proposed a blindagainst watermarking various attacks scheme [25,39]. which In order uses to modified achieve the listless merit SPIHTof both CRT (NLS) and that listless combines SPIHT, we with DCT coefficientsproposed and CRTa blin encryptiond watermarking technique. scheme which uses modified listless SPIHT (NLS) that combines with DCT coefficients and CRT encryption technique. 3. Proposed Scheme 3. Proposed Scheme The blockThe diagramblock diagram of the of the proposed proposed scheme scheme is shown shown in inFigure Figure 1. The1. The uniqueness uniqueness of the ofscheme the scheme is that theis that key the is key generated is generated randomly randomly during during embeddingembedding process process instead instead of using of using a known a known key for key for embeddingembedding in comparison in comparison to other to other methods. methods. The The generated generated key key is is required required to tosend send to the to thereceiver receiver for watermarkfor watermark extraction. extraction.

Figure 1.FigureWatermark 1. Watermark embedding embedding process; process; DCT:DCT: Discrete discrete cosine cosine transform transform;; CRT: CRT:Chinese Chinese remainder remainder theorem;theorem SPIHT:; SPIHT: set partitioning set partitioning in hierarchicalin hierarchical trees.trees.

The embedding and extraction technique of the proposed method is based on combining the TheCRT embedding technique in and the extractionDCT domain. technique The listless of SPIHT the proposedcompression method scheme is used based to ongenerate combining an the CRT techniqueembedded in compressed the DCT domain.bit string which The listlesscan be used SPIHT for compressionstorage and/or transmission. scheme is used This method to generate an embeddedadded compressed the flexibility bit to stringcompress which losslessly can beor lossy used by for setting storage a target and/or bit rate. transmission. In addition, b Thisetter method added thesecurity ﬂexibility and better to compress robustness losslesslyis achievedor during lossy the by extraction setting a phase. target We bit have rate. presented In addition, the better detailed algorithm for embedding and extraction in the following subsections. security and better robustness is achieved during the extraction phase. We have presented the detailed algorithm3.1. forEmbedding embedding Procedure and extraction in the following subsections. The following steps describe the watermark embedding procedure on the host image. 3.1. Embedding Procedure Step-1 Partition of blocks The following steps describe the watermark embedding procedure on the host image. The host image is taken as a grayscale image and watermark is a binary image. Initially, the host Step-1image Partition is divided of blocks into blocks of 8 × 8 pixels. The single watermark bit is then embedded in a single block selected. Then the blocks are chosen as random. The host image is taken as a grayscale image and watermark is a binary image. Initially, the host image is divided into blocks of 8 × 8 pixels. The single watermark bit is then embedded in a single block selected. Then the blocks are chosen as random. Future Internet 2017, 9, 33 4 of 16

Step-2 Selection of DCT coefficients In order to improve robustness, the preferred choice is to embed the watermark into the most significant coefficients. However, it will degrade the quality of the watermark. If insignificant coefficients are used for embedding, the watermark may be lost during lossy compression. Since proposed scheme use block size 8 × 8 pixels, the resulting DCT block is also of the same size. This gives a total of 64 DCT coefficients. We have included both DC and low-frequency AC coefficients for embedding in order to achieve trade-off between quality and robustness. The DC component and the remaining 63 low-frequency AC components are selected as the possible locations for embedding the watermark bits in the DCT domain. Step-3 Zigzag scanning of the DCT coefficients After zigzagging the DCT coefficients, one value must be selected. During that selection, randomizing the coefficients is done using a pseudo random generator. Using randomization technique, security and authentication aspects will increase as the value is changing for every execution. During randomization, one of the selected coefficients is chosen and its index is stored as a secret key whenever we execute the code. This feature adds more security to the data while embedding the procedure. Step-4 Arnold transform This is a digital scrambling algorithm to randomize the bits of the watermark. It improves the robustness of the digital watermark. In order to increase the security and robustness of watermark, we use Torus automorphism (TA), which could disperse a watermark equally and randomly [41]. Applying TA for scrambling the binary watermark before embedding offers cryptographic protection against intentional reconstruction of watermark [39]. Further, the key utilized in the TA permutation procedure for scrambling the watermark is also required during the inverse TA permutation (descrambling) procedure. The TA permutation procedure is based on the following equation [39]. ! ! ! i∗ 1 1 i = × modm, (1) j∗ k k + 1 j

Equation (1) indicates that each pixel of the watermark pattern at coordinate (i, j) will move to (i∗, j∗). The k and m are the key parameters given by the user. Step-5 Embedding the watermark bits After dividing 8 × 8 blocks, one watermark bit gets embedded in each of the block. Considering one block at a time, the procedure for embedding the watermark bits is listed as follows:

• Randomly select an 8 × 8 block from host image and apply DCT to the block. • Randomly select a watermark bit from the watermark image in order to embed into the block. • Randomly select a DCT coefﬁcient (DC or one of the AC components) to embed the watermark bit. Let its value be denoted as ϑ.

• Let P1 and P2 be the pair-wise co-prime numbers used in CRT (say, P1 = 38 and P2 = 107) for the DC coefﬁcient. For an AC coefﬁcient, let the values of P1 = 38 and P2 = 55. • Find E1 and E2 by applying the inverse CRT to ϑ. Let the absolute difference between E1 and E2 value be ‘z’.

• Determine the value D = max(P1,P2) – 1. • The required condition for embedding watermark bit ‘1’, is

z ≥ D/u, (2)

where u = 2 if ϑ is DC coefficient, otherwise, u = 3. Apply CRT to modify ϑ. If the condition is not satisfied, then ϑ is modified in step of 8 until the required condition in Equation (2) is satisfied. Future Internet 2017, 9, 33 5 of 16

• The required condition for embedding watermark bit ‘0’ is

z < D/u, (3)

• Apply CRT to modify ϑ. If the condition in Equation (3) is not satisfied, then ϑ is modified in step 8 until the required condition is satisfied. • Reconstruct DCT block with the modified DCT coefficient ϑˆ and apply inverse DCT to the block to construct the watermarked image block. • Repeat the steps (represented in bullet form) 1–9 for all the remaining blocks until all the watermark bits are embedded.

Note that the range of possible values for DC and the AC coefficients are not the same. The range of the DC coefficient is from values 0 to 2040, while for AC coefficients, it will be from the −1020 to +1020 in monochrome images. According to CRT, the product of the pair-wise co-prime numbers (also called the dynamic range) must be greater than the largest possible number [25]. The block diagram for embedding procedure is shown in Figure1. Step-6 Hierarchical Pyramid-like coefficient arrangement: Figure2 shows an arrangement of the DCT coefficients in a hierarchical pyramid structure. The coefficients in each 8 × 8 block are arranged in a parent-child relationship. The parent of a j i k th coefficient i is 4 for 4 ≤ i ≤ 63, while the set of four children associated with j coefficient is {4j, 4j + 1, 4j + 2, 4j + 3} for 0 ≤ j ≤ 15. The DC coefficient indexed as 0 is the root of the tree, which has only three children, e.g., 1, 2 and 3. The offspring corresponds to direct descendent in the same spatial location in the next finer band of pyramid. A tree corresponds to a node having 4 children which always form a group of 2 × 2 adjacent pixels. In the bottom part of the Figure2, arrows indicate the same indexed coefficients of other blocks those are grouped together so as to form an overall 3-level pyramid structure. Step-7 Listless SPIHT compression algorithm SPIHT is the benchmark algorithm for compression of still images. It makes use of ordered auxiliary lists to encode significant information in a bit plane manner. The movement of list nodes, addition of new nodes to the list and deletion of a node from the list while processing the ordered bit planes in SPIHT causes a lot of memory management problems. To overcome this listless variant of SPIHT, called NLS is proposed in [36]. NLS reduces memory management problem by using state table markers for encoding coefficients. Further, NLS need less dynamic memory storage for the image. The proposed watermark algorithm makes use of modified NLS for compressing the image form lossy to lossless. Precise rate control can be achieved while compression is lossy. The modified NLS is similar to NLS except that the refinement pass is executed prior to sorting pass. This ensures better compression performance in modified NLS. Future Internet 2017, 9, 33 6 of 16 Future Internet 2017, 9, 33 6 of 16

FigureFigure2. 2. CoefﬁcientCoefficient rearrange algorithm algorithm..

3.2.Step Extraction-7 Listless Procedure SPIHT compression algorithm TheSPIHT procedure is the of benchmark extraction algorithm is the reverse for compression of embedding of procedure. still images. Figure It makes3 shows use the of ordered extraction process.auxiliary The lists detailed to encode of watermark significant extraction information process in a bit is plane outlined manner. as follows: The movement of list nodes, addition of new nodes to the list and deletion of a node from the list while processing the ordered bit Step-1planes Listless in SPIHT SPIHT causes decoding a lot of memory management problems. To overcome this listless variant of SPIHTSPIHT, called decodes NLS the is proposed image form in the[34]. compressed NLS reduces bit memory string. Itmanagement follows the problem exact reverse by using process state that table markers for encoding coefficients. Further, NLS need less dynamic memory storage for the the SPIHT encoder does [36,37]. image. The proposed watermark algorithm makes use of modified NLS for compressing the image Step-2form lossy Coefficient to lossless. restoration Precise rate algorithm control can be achieved while compression is lossy. The modified NLS is similar to NLS except that the refinement pass is executed prior to sorting pass. This ensures This process restore the coefficients back into the original position, i.e., individual 8 × 8 blocks of better compression performance in modified NLS. coefficients that has been arranged into hierarchical manner before SPIHT encoding. Step-33.2. Extraction IDCT Procedure InverseThe procedure DCT is applied of extraction in to all the is the8 × reverse8 blocks of to embeddingfind the image procedure. pixels and Figure merge 3 all shows 8 × 8 the block to constructextraction aprocess. large compressed The detailed decoded of watermark watermarked extraction image process (512 is outlined× 512 in as this follows: case).

FutureFuture Internet Internet2017 2017, 9,, 9 33, 33 7 of 167 of 16

FigureFigure 3. WatermarkWatermark extraction extraction process process..

Step-4Step-1 ExtractionListless SPIHT of watermark decoding ForSPIHT this, decodes following the informationimage form the must compressed be known bit string. to get It the follows watermark the exact extracted reverse process from the that the SPIHT encoder does [34–36]. watermarked image: (i) Image which is watermarked; (ii) Size of watermark; (iii) Watermark Key; and (iv)Step Pair-wise-2 Coefficient numbers restoration of co-prime algorithmP1 and P2. WithThis knowledge process restore of the the watermarkcoefficients back key, theinto DCTthe original coefficient position, that i.e. is, embedded individual 8 with × 8 blocks watermark of ˆ ˆ informationcoefficientsϑ thatis extracted. has been arranged Thus, with into values hierarchical of P1, Pmanner2 and ϑ before, the E SPIHT1, E2 are encoding determined.. Next, using a CRT scheme, a comparison is made between the z and D. If z ≥ D/u, the bit 1 would be extracted, Step-3 IDCT otherwise the bit 0 would be get extracted. The scale factor u is then set to 2 for the coefficient of DC, otherwiseInverse it is getDCT set is toapplied 3. Thus, in to the allabove the 8 × steps 8 blocks were to thenfind the repeated image untilpixels every and merge block all consecutively 8 × 8 block to getto all construct watermark a large bits compressed extracted. decoded watermarked image (512 × 512 in this case). StepIn-4 order Extraction to improve of watermark robustness, the preferred choice is to embed the watermark into the most significant coefficients. However, it will degrade the quality of the watermark. If insignificant For this, following information must be known to get the watermark extracted from the coefficients are used for embedding, the watermark may have been lost during lossy compression. watermarked image: (i) Image which is watermarked; (ii) Size of watermark; (iii) Watermark Key; Since the proposed scheme use block-size 8 × 8 pixels, the resulting DCT block is also of same size. and (iv) Pair-wise numbers of co-prime P1 and P2. This gives a total of 64 DCT coefficients. With knowledge of the watermark key, the DCT coefficient that is embedded with watermark ˆ ˆ 3.3.information Secret Keys  is extracted. Thus, with values of P1, P2 and , the E1, E2 are determined. Next, using a CRT scheme, a comparison is made between the z and D. If z  D / u, the bit 1 would be extracted,Secrete otherwise keys are vitalthe bit for 0 wouldthe proposed be get extracted algorithm. The to scale implement factor u embedding is then set and to 2 forextraction the procedures.coefficient Threeof DC, keys otherwise are utilized it is get and set these to 3. shouldThus, the be above preserved steps wellwere for then watermark repeated extractionuntil everyand verification.block consecutively These are: to get all watermark bits extracted. In order to improve robustness, the preferred choice is to embed the watermark into the most • significantThe parameters coefficients. for Torus However, automorphism it will degrade permutation: the quality The of disarrangement the watermark. parameters If insignificantk and m coefficientsobtained are in Equationused for embedding, (1) are necessary the watermark when reconstructing may have been the lost watermark during lossy pattern. compression. • SinceThe the record proposed number scheme of the use selected block-size block 8 × has8 pixels, to be the preserved. resulting DCT block is also of same size. • ThisThe gives embedding a total of position64 DCT coefficients. of the watermark bits, i.e., the DCT coefficients and their positions have to be preserved. • 3.3.Co-prime Secret Keys numbers used in CRT. Secrete keys are vital for the proposed algorithm to implement embedding and extraction 4. Simulationprocedures. Three Results keys and are Analysis utilized and these should be preserved well for watermark extraction andThe verification. experiments These have are been: conducted on test images of size 512 × 512 pixels. MATLAB 14.1 tool is used The for parameters experiments for on Torus an Intelautomorphism core i5 processor, permutation: 2.6 GHz The disarrangement CPU and 6 GB RAM.parameters The watermarkk and used inm the obtained experiment in Equation is a binary (1) imageare necessary of 62 × when64 pixels. reconstructing In order to the validate watermark the objective pattern. judgement of the extracting fidelity, the performance comparison of the proposed method is compared with other

Future Internet 2017, 9, 33 8 of 16 schemes in terms of PSNR, Tamper assessment function (TAF) and Normalized Correlation (NC). For a watermark of size m × n, the TAF in percentage is deﬁned as: " # 1 m n TAF(%) = ∑ ∑ w(i, j) ⊕ w(i, j) × 100, (4) mn i=0 j=0 and, Normalized Correlation (NC) is deﬁned as:

m n ∑ ∑ w(i, j) × w(i, j) i=0 j=0 NC = m n , (5) 2 ∑ ∑ [w(i, j)] i=0 j=0 where w(i, j) and w(i, j) represent the original and extracted watermarks at position (i, j), respectively; and, ⊕ is exclusive or operation. TAF represents the number of bits of the extracted watermark that are different from the original watermark. The acceptance level of TAF is usually 20% as the extracted watermark is not recognizable beyond this value. Similarly, the NC value is 1 for highest-quality watermark and 0 for lowest-quality watermark. Watermark having NC below 0.3–0.4 is not recognizable. PSNR is a quantitative measure of image quality between original host image and watermarked image. The value of PSNR usually ranges between 20 dB (low quality) and 40 dB (high quality). For 8-bit grayscale images PSNR is given as:    2552  PSNR(dB) = 10 log  , (6) 10 M N   1 2  MN ∑ ∑ [X(i, j) − X(i, j)] i=1 j=1 where X represents the host image, and X represents the watermarked image. M × N represents the size of the two images. Structural similarity (SSIM) index is used for measuring similarity between two images. SSIM is designed to improve on traditional methods such as PSNR, which has been proven to be inconsistent with human visual perception. The measure between two images X and X of same size M × N is given by: (2µ µ + c )(2σ + c ) SSIM = x x 1 xx 2 , (7) 2 2 2 2 (µx + µx + c1)(σx + σx + c2) 2 2 where µx and σx are the average and variance of image X respectively, µx and σx are the average and variance of watermarked image X respectively. σxx is the covariance. c1 and c2 are two variables to stabilize the denominator. Its range is from 0 to 1. If two images are perfectly matched, SSIM = 1. The performance comparison is carried out considering the following performance criteria:

• Quality of the watermarked images. • Robustness of the scheme against common attacks such as compression, crop, histogram manipulation, auto contrast, noise, inversion and ﬁltering.

4.1. Quality of the Watermarked Images Tables1–3 shows the compression performance comparison on the watermarked images for Lena, Barbara, and Boat images, respectively, using SPIHT, LSK and the proposed algorithm. The PSNR and SSIM performance is quite satisfactory below a BR (bit rate) of 0.25 bits per pixel (bpp). The corresponding compression ratio (CR) can be found using the formula CR = 8/BR, (i.e., CR = 32:1). The TAF (in %) and NC values are also listed. It can be noted from the Tables1–3 that the Future Internet 2017, 9, 33 9 of 16 extracted watermark using all the three algorithms are not recognizable above a BR of 0.25 bpp. As we increase the bit rate, the quality of the watermark and watermarked images is also improved. It can be seen from the Tables1–3 that the proposed scheme provides better watermark quality and better compressed image quality at lossless compression mode for Lena, Barbara and Boat images. In lossy compression mode, our proposed scheme is highly competitive (with respect to watermarked image and the quality of extracted watermark) with SPIHT and LSK in most of the BR. A comparison of the extracted watermark quality is ﬁgured out in the Figures4–6 for Lena, Mandrill, and Boat images. For the Lena image (Figure4), the proposed scheme outperforms NC and TAF values compared to the work by Patra et al. [25] at all compression ratios. Additionally, our scheme outperforms Das et al. [40] above 12:1 compression ratios. The similar improvements are also observed for Mandrill and Boat images in Figures5 and6, respectively. We have not shown the comparison of TAF and NC values for Mandrill and Boat images due to unavailability of experimental datum by Das et al. [40]. Figure7 shows the PSNR vs. Compression ratio for Mandrill, Lena, and Boat images. It is observed that for Mandrill image, our scheme outperforms the scheme by Patra et al. [25] in all CRs, i.e., the quality of the watermarked image is better using our scheme. However, a similar trend is not visualized in Lena and Boat images for all CRs. This is because our scheme provides better robustness and compression performance for texture images than smooth images.

Table 1. PSNR (Peak signal to noise ratio), structural similarity (SSIM), tamper assessment function (TAF), and Normalized Correlation (NC) performance on the Lena Image’; SPIHT: set partitioning in hierarchical trees; LSK: Listless SPECK. Bolded numbers indicate improvement in results.

Image Bit Rate (bpp) Algorithm PSNR (dB) SSIM NC TAF (%) SPIHT [35] 31.24 0.9075 0.1317 65.15 0.25 LSK [38] 30.93 0.8974 0.3032 52.75 Proposed 30.93 0.8982 0.3201 51.61 SPIHT 31.10 0.9167 0.9848 1.71 0.5 LSK 31.31 0.9301 0.9828 2.04 Proposed 31.36 0.9292 0.9848 1.86 SPIHT 33.87 0.9600 0.9801 1.84 1.0 LSK 34.02 0.9621 0.9797 1.91 Proposed 34.13 0.9617 0.9804 1.94 SPIHT 34.35 0.9658 0.9227 6.14 Lena 1.5 LSK 34.76 0.9677 0.9180 6.52 Proposed 34.40 0.9676 0.9149 6.32 2.0 SPIHT 34.83 0.9686 0.9298 5.56 LSK 34.76 0.9677 0.9180 6.52 Proposed 34.90 0.9693 0.9257 5.26 2.5 SPIHT 35.41 0.9738 0.9510 4.00 LSK 35.33 0.9733 0.9517 4.08 Proposed 35.44 0.9740 0.9598 4.07 3.0 SPIHT 35.65 0.9724 0.9490 4.08 LSK 35.52 0.9744 0.9480 4.28 Proposed 35.65 0.9739 0.9558 3.27 Lossless SPIHT 35.83 0.9762 0.9841 1.36 LSK 35.68 0.9754 0.9747 2.24 Proposed 36.00 0.9750 0.9831 1.36 Future Internet 2017, 9, 33 10 of 16

Table 2. PSNR, SSIM, TAF and NC performance on the Barbara Image.

Image Bit Rate (bpp) Algorithm PSNR (dB) SSIM NC TAF (%) SPIHT [35] 26.92 0.8454 0.0300 72.93 0.25 LSK [38] 27.26 0.8749 0.0527 71.27 Proposed 27.25 0.8730 0.0700 70.33 SPIHT 29.42 0.9106 0.3788 47.58 0.5 LSK 29.03 0.9047 0.7539 19.63 Proposed 28.97 0.9034 0.7762 17.94 SPIHT 31.48 0.9474 0.9784 3.09 1.0 LSK 32.16 0.9572 0.9673 3.57 Proposed 32.25 0.9572 0.9737 3.27 SPIHT 34.27 0.9714 0.9737 2.84 Barbara 1.5 LSK 34.43 0.9731 0.9689 3.25 Proposed 34.50 0.9731 0.9659 3.57 2.0 SPIHT 34.77 0.9774 0.9034 7.68 LSK 34.78 0.9775 0.8974 8.19 Proposed 34.83 0.9784 0.9045 7.38 2.5 SPIHT 35.52 0.9801 0.9355 5.56 LSK 35.25 0.9800 0.9136 6.98 Proposed 35.54 0.9804 0.9416 5.26 3.0 SPIHT 35.77 0.9821 0.9521 4.15 LSK 35.66 0.9830 0.9477 4.41 Proposed 35.86 0.9824 0.9440 4.73 Lossless SPIHT 36.06 0.9822 0.9791 2.04 LSK 35.66 0.9814 0.9443 4.58 Proposed 36.24 0.9827 0.9770 2.19

Table 3. PSNR, SSIM, TAF and NC performance on the Boat Image.

Image Bit Rate (bpp) Algorithm PSNR (dB) SSIM NC TAF (%) SPIHT [35] 29.34 0.9066 0.078 69.70 0.25 LSK [38] 29.20 0.9024 0.1425 64.76 Proposed 29.21 0.9036 0.1610 63.43 SPIHT 29.61 0.9085 0.9814 2.31 0.5 LSK 30.04 0.9244 0.9791 2.41 Proposed 30.10 0.9241 0.9801 2.41 SPIHT 33.20 0.9599 0.9801 2.31 1.0 LSK 33.42 0.9660 0.9743 2.64 Proposed 33.44 0.9641 0.9757 2.36 SPIHT 34.22 0.9648 0.9119 6.95 Boat 1.5 LSK 34.32 0.9655 0.9088 7.15 Proposed 34.34 0.9690 0.9136 6.75 2.0 SPIHT 34.87 0.9693 0.9055 7.40 LSK 34.80 0.9702 0.9058 7.56 Proposed 34.94 0.9709 0.9099 7.18 2.5 SPIHT 35.53 0.9732 0.9440 4.81 LSK 35.44 0.9745 0.9470 4.41 Proposed 35.52 0.9751 0.9456 4.51 3.0 SPIHT 35.70 0.9758 0.9564 3.48 LSK 35.60 0.9738 0.9396 4.88 Proposed 35.75 0.9756 0.9537 3.88 Lossless SPIHT 35.92 0.9758 0.9804 1.89 LSK 35.78 0.9747 0.9656 2.79 Proposed 36.10 0.9760 0.9811 1.73 Future Internet 2017, 9, 33 11 of 16

LSK 35.66 0.9830 0.9477 4.41 Proposed 35.86 0.9824 0.9440 4.73 Lossless SPIHT 36.06 0.9822 0.9791 2.04 LSK 35.66 0.9814 0.9443 4.58 Proposed 36.24 0.9827 0.9770 2.19

Table 3. PSNR, SSIM, TAF and NC performance on the Boat Image.

Image Bit Rate (bpp) Algorithm PSNR (dB) SSIM NC TAF (%) SPIHT [36] 29.34 0.9066 0.078 69.70 0.25 LSK [34] 29.20 0.9024 0.1425 64.76 Proposed 29.21 0.9036 0.1610 63.43 SPIHT 29.61 0.9085 0.9814 2.31 0.5 LSK 30.04 0.9244 0.9791 2.41 Proposed 30.10 0.9241 0.9801 2.41 SPIHT 33.20 0.9599 0.9801 2.31 1.0 LSK 33.42 0.9660 0.9743 2.64 Proposed 33.44 0.9641 0.9757 2.36 SPIHT 34.22 0.9648 0.9119 6.95 1.5 LSK 34.32 0.9655 0.9088 7.15 Proposed 34.34 0.9690 0.9136 6.75 Boat 2.0 SPIHT 34.87 0.9693 0.9055 7.40 LSK 34.80 0.9702 0.9058 7.56 Proposed 34.94 0.9709 0.9099 7.18 2.5 SPIHT 35.53 0.9732 0.9440 4.81 LSK 35.44 0.9745 0.9470 4.41 Proposed 35.52 0.9751 0.9456 4.51 3.0 SPIHT 35.70 0.9758 0.9564 3.48 LSK 35.60 0.9738 0.9396 4.88 Proposed 35.75 0.9756 0.9537 3.88 Lossless SPIHT 35.92 0.9758 0.9804 1.89 Future Internet 2017, 9, 33 LSK 35.78 0.9747 0.9656 2.79 11 of 16 Proposed 36.10 0.9760 0.9811 1.73

ComparisonComparison of TAF of TAF and and NC NC values values on JPEG on JPEG compression compression attacks: attacks:

(a) (b)

FigureFigure 4. Performance4. Performance of of compressioncompression attack attack on on Lena Lena image image on on(a) (NCa) NC vs. Compression vs. Compression ratio; ratio;and (b and) TAF vs. Compression ratio. Future(b) TAF Internet vs. 2017 Compression, 9, 33 ratio. 12 of 16 Future Internet 2017, 9, 33 12 of 16

(a) (b) (a) (b) FigureFigure 5. Performance 5. Performance of compressionof compression attack attack on on Mandrill Mandrill image image on on (a )(a NC) NC vs. vs. Compression Compression ratio; ratio and; andFigure (b) 5.TAF Performance vs. Compression of compression ratio. attack on Mandrill image on (a) NC vs. Compression ratio; (b) TAFand ( vs.b) TAF Compression vs. Compression ratio. ratio.

(a) (b) (a) (b) Figure 6. Performance of compression attack on Boat image on (a) NC vs. Compression ratio; and (b) FigureTAFFigure6. vs. Performance6. Compression Performance ofratio. of compression compression attack on on Boat Boat image image on on (a)( NCa) NC vs. vs.Compression Compression ratio; ratio;and (b and) (b) TAF vs. Compression Compression ratio. ratio.

(a) (b) (c) (a) (b) (c) Figure 7. PSNR vs. Compression ratio on (a) Mandrill; (b) Lena, and; (c) Boat images. Figure 7. PSNR vs. Compression ratio on (a) Mandrill; (b) Lena, and; (c) Boat images. 4.2. Robustness after Different Attacks 4.2. Robustness after Different Attacks Table 4 shows the quality of the extracted watermark after some conventional attacks such as cropping,Table addition4 shows the of quality noise, sharpening,of the extracted inversion, watermark histogram after some equalization conventional and attacks auto contrastsuch as operations.cropping, additionIt observed of that noise, the sharpening,proposed method inversion, could histogramable to extract equalization a good quality and autowatermark contrast in alloperations. cases. Image It observed inversion that and the autoproposed contrast method operation could giveable betterto extract quality a good watermark quality watermark with an NC in valueall cases. close Image to 1. Acceptable inversion andquality auto watermark contrast operation is also obtained give better in cropping quality attack, watermark masking with face an with NC avalue black close box, to cropping 1. Acceptable a quart qualityer part watermark of the image is also on obtained any direction, in cropping noise attack,and sharpen masking operations. face with Similara black acceptablebox, cropping quality a quart watermarker part ofcan the also image be obtained on any direction,by masking noise the andimage sharpen with a operations.white box. TheSimilar PSNR acceptable values indicate quality watermarkthe extracted can quality also be of obtained the watermarked by masking images. the image PSNR with values a white of 3 5box.–36 dBThe indicate PSNR valuesgood quality indicate watermarked the extracted images quality are of also the obtainedwatermarked in most images. of the PSNR attacks. values of 35–36 dB indicate good quality watermarked images are also obtained in most of the attacks. Table 4. Simulation results of common image processing attacks. Table 4. Simulation results of common image processing attacks.

Future Internet 2017, 9, 33 12 of 16

(a)

(b)

(c)

Figure 7. PSNR vs. Compression ratio on (a) Mandrill; (b) Lena; (c) Boat images.

4.2. Robustness after Different Attacks Table4 shows the quality of the extracted watermark after some conventional attacks such as cropping, addition of noise, sharpening, inversion, histogram equalization and auto contrast operations. It observed that the proposed method could able to extract a good quality watermark in all cases. Image inversion and auto contrast operation give better quality watermark with an NC value close to 1. Acceptable quality watermark is also obtained in cropping attack, masking face with a black box, cropping a quarter part of the image on any direction, noise and sharpen operations. Similar acceptable quality watermark can also be obtained by masking the image with a white box. The PSNR values indicate the extracted quality of the watermarked images. PSNR values of 35–36 dB indicate good quality watermarked images are also obtained in most of the attacks. Future Internet 2017, 9, 33 NoiseNoiseNoise Noise HistogramHistogramHistogramHistogram 13 of 16 AttacksAttacksAttacksAttacks CropCropCrop Crop NoiseNoiseNoise Noise SharpenSharpenSharpenSharpen InvertInvertInvertInvert HistogramHistogramHistogramHistogram AttacksAttacksAttacksAttacks CropCropCrop Crop (Impulse)(Impulse)(Impulse)(Impulse) SharpenSharpenSharpenSharpen InvertInvertInvertInvert EqualizeEqualizeEqualizeEqualize (Impulse)(Impulse)(Impulse)(Impulse) EqualizeEqualizeEqualizeEqualize Table 4. SimulationNoiseNoise resultsNoise Noise of common image processing attacks. HistogramHistogramHistogramHistogram AttacksAttacksAttacksAttacks CropCropCrop Crop NoiseNoiseNoise Noise SharpenSharpenSharpenSharpen InvertInvertInvertInvert HistogramHistogramHistogramHistogram AttacksAttacksAttacksAttacks CropCropCrop Crop (Impulse)(Impulse)(Impulse)(Impulse) SharpenSharpenSharpenSharpen InvertInvertInvertInvert EqualizeEqualizeEqualizeEqualize Noise(Impulse) HistogramEqualize AttackedAttacksAttackedAttackedAttacked Crop (Impulse)(Impulse)(Impulse) Sharpen Invert EqualizeEqualizeEqualize AttackedAttackedAttackedAttacked (Impulse) Equalize ImagesImagesImagesImages ImagesImagesImagesImages NoiseNoiseNoiseNoise HistogramHistogramHistogramHistogram AttackedAttackedAttacksAttacksAttackedAttacksAttackedAttacks CropCropCropCrop NoiseNoiseNoiseNoiseNoise SharpenSharpenSharpenSharpen InvertInvertInvertInvertInvert HistogramHistogramHistogramHistogramHistogram AttackedAttackedAttacksAttacksAttackedAttacksAttacksAttackedAttacks CropCropCropCropCrop (Impulse) (Impulse)Noise(Impulse)(Impulse)NoiseNoise(Impulse)NoiseNoise SharpenSharpenSharpenSharpenSharpen InvertInvertInvertInvertInvertInvert Histogram EqualizeHistogramHistogramEqualizeHistogramEqualizeHistogramEqualize AttackedAttacksImagesAttacksImagesAttacksAttacksAttacksImagesImages CropCropCropCropCrop (Impulse)(Impulse)(Impulse)(Impulse)(Impulse)(Impulse) SharpenSharpenSharpenSharpenSharpen InvertInvertInvertInvertInvertInvert EqualizeEqualizeEqualizeEqualizeEqualize PSNR(dB)PSNR(dB)ImagesImagesPSNR(dB)ImagesPSNR(dB)ImagesImages 35.835.8 35.8 35.8 (Impulse)35.9(Impulse)(Impulse)35.9(Impulse)(Impulse)(Impulse) 35.9 35.9 37.937.9 37.9 37.9 35.935.9 35.9 35.9 Equalize34.934.9EqualizeEqualizeEqualize Equalize34.9 34.9 PSNR(dB)PSNR(dB)PSNR(dB)PSNR(dB) 35.835.835.8 35.8 35.935.935.9 35.9 37.937.937.9 37.9 35.935.935.9 35.9 34.934.934.9 34.9 ExtractedExtractedExtractedExtracted ExtractedExtractedExtractedExtracted PSNR(dB)PSNR(dB)WatermAttackedWatermPSNR(dB)AttackedPSNR(dB)AttackedAttackedWatermWaterm 35.835.8 35.8 35.8 35.935.9 35.9 35.9 37.937.9 37.9 37.9 35.935.9 35.9 35.9 34.934.9 34.9 34.9 PSNRAttackedAttackedAttackedAttacked (dB)Attacked 35.8 35.9 37.9 35.9 34.9 PSNR(dB)PSNR(dB)AttackedWatermPSNR(dB)WatermPSNR(dB)WatermWaterm 35.835.835.8 35.8 35.935.935.9 35.9 37.937.937.9 37.9 35.935.935.9 35.9 34.934.934.9 34.9 ExtractedExtractedAttackedAttackedExtractedarkImagesAttackedExtractedarkAttackedImages ImagesImages arkImages ark arkImagesarkImagesImages ImagesImagesark Imagesark ExtractedExtractedExtractedExtractedImagesExtractedImages Images WatermNCWatermNC ImagesWatermNC ImagesWatermImagesNC 0.71580.71580.71580.7158 0.7208 0.7208 0.72080.7208 0.5034 0.50340.50340.5034 0.9771 0.97710.97710.9771 0.5415 0.54150.54150.5415 WatermarkNCWatermNC NC WatermNC 0.71580.71580.71580.7158 0.72080.7208 0.72080.7208 0.5034 0.50340.50340.5034 0.9771 0.97710.97710.9771 0.54150.54150.54150.5415 WatermWaterm arkark arkark CropCropCrop (topCrop (top (top (top CropCrop Crop (topCrop (top (top (top Crop Crop Crop (bottomCrop (bottom (bottom (bottom Crop Crop Crop (bottomCrop (bottom (bottom (bottom AttacksPSNR(dB)AttacksPSNR(dB)PSNR(dB)arkAttacksPSNR(dB)arkAttacks ark ark Crop CropCrop35.8 (top 35.8Crop(top35.8 35.8 (top (top CropCropCrop35.9 (top 35.9Crop(top35.9 35.9 (top (top CropCrop Crop (bottom Crop(bottom37.937.9 37.9(bottom 37.9 (bottom Crop Crop Crop (bottom Crop(bottom35.935.9 35.9(bottom 35.9 (bottom Auto Auto Auto contrastAuto contrast34.934.9 34.9contrast 34.9contrast AttacksPSNR(dB)AttacksPSNR(dB)NCPSNR(dB)NCPSNR(dB)AttacksPSNR(dB)NC Attacks 0.71580.715835.80.715835.835.835.8 35.8 0.72080.720835.90.720835.935.935.9 35.9 0.5034 0.503437.90.503437.937.937.9 37.9 0.977135.90.977135.9 0.977135.935.9 35.9 AutoAutoAuto0.5415 contrast Auto34.9contrast0.541534.934.9 34.9contrast 0.5415 34.9 contrast PSNR(dB)NCPSNR(dB)PSNR(dB) NC 0.715835.8left)left)35.80.7158 left)35.8 left) 0.7208right)35.9right)35.90.7208 right) 35.9 right) 0.503437.9left)left)37.90.5034 left)37.9 left) 0.9771right)35.9right)35.90.9771 right) 35.9 right) 0.541534.934.90.5415 34.9 AttacksExtractedNCPSNR(dB)ExtractedNCPSNR(dB)Extracted ExtractedNC NC Crop 0.71580.7158 (topleft)0.7158left)35.8 left)0.715835.8 left) left) Crop 0.7208 (top0.7208right)right)0.720835.9 right)0.720835.9 right) right) Crop 0.50340.5034 (bottomleft)0.5034left)37.90.503437.9 left) left) left) Crop0.97710.9771right)right) (bottom0.977135.90.977135.9 right) right) right) 0.54150.5415 Auto0.541534.90.541534.9 contrast ExtractedExtractedExtractedExtractedExtracted Crop CropCrop (topCrop (top (top (top CropCropCrop (topCrop (top (top (top Crop CropCrop (bottomCrop (bottom (bottom (bottom Crop Crop Crop (bottomCrop (bottom (bottom (bottom AttacksExtractedAttacksExtractedExtractedWatermExtractedAttacksExtractedWatermAttacksWaterm Waterm AutoAutoAuto contrastAuto contrast contrast contrast WatermWatermWatermWatermWatermCropCropCrop (top Crop(top (top (top CropCropCrop (top Crop(top (top (top Crop CropCrop (bottom Crop(bottom (bottom (bottom Crop Crop Crop (bottom Crop(bottom (bottom (bottom AttacksAttacksWatermAttacksAttacks left)left) left) left) right)right)right) right) left)left) left) left) right)right)right) right) Auto AutoAuto contrast Autocontrast contrast contrast AttackedAttackedAttackedAttackedWatermWatermarkWaterm arkWaterm ark ark left)left) left) left) right)right)right) right) left)left) left) left) right)right)right) right) AttackedAttackedAttackedAttackedarkarkark arkark ImagesImagesNCarkNCImagesNC arkImagesarkNC arkark ark 0.71580.71580.71580.7158 0.72080.72080.72080.7208 0.50340.50340.50340.5034 0.97710.97710.97710.9771 0.54150.54150.54150.5415 ImagesImagesImagesNCNCNCImagesNC ImagesNC 0.71580.71580.71580.71580.7158 0.72080.72080.72080.72080.7208 0.50340.50340.50340.50340.5034 0.97710.97710.97710.97710.9771 0.54150.54150.54150.54150.5415 NCNCNC NCNC Crop0.7158Crop0.71580.7158CropCrop0.7158 (top0.7158 (top (top(top (top Crop0.7208Crop0.72080.7208CropCrop0.7208 (top0.7208 (top (top(top (top CropCrop0.5034CropCrop0.50340.5034 (bottom0.5034 (bottom0.5034 (bottom(bottom (bottom Crop Crop0.9771CropCrop0.97710.9771 (bottom0.9771 (bottom0.9771 (bottom(bottom (bottom 0.54150.54150.54150.54150.5415 AttackedAttackedAttackedAttacked AttackedAttackedAttacksAttackedAttacksAttacksAttackedAttacks CropCropCropCropCrop (top (top(top (top(top (top CropCropCropCropCrop (top (top(top (top(top (top CropCropCropCropCrop (bottom (bottom(bottom (bottom(bottom (bottom Crop CropCropCropCrop (bottom (bottom(bottom (bottom(bottom (bottom Auto AutoAutoAuto contrast contrast contrast contrast ImagesAttacksImagesAttacksAttacksAttacksImagesAttacksImages CropCropCropCropCrop left)(top left) left)(top(topleft)left) (top (top left) (top Crop CropCropCropright)Crop (topright) (topright)(topright) right)(top(top (top Crop CropCrop CropCrop (bottom left)(bottom(bottom left)left)(bottom(bottom left)(bottomleft) left) Crop Crop CropCropCrop (bottomright) (bottom(bottomright) (bottom(bottomright)right) (bottomright) Auto Auto AutoAutoAuto contrast contrastcontrast contrast contrast PSNR(dB)PSNR(dB)AttacksPSNR(dB)AttacksAttacksPSNR(dB)AttacksAttacks 35.835.8 left)35.8 35.8left)left)left)left) left) 35.835.8right) 35.8right) right)35.8right)right)right) 35.8435.8435.84left) 35.84left) left)left)left) left) 35.8635.86right)35.86 right)35.86right) right)right)right) AutoAuto35.82Auto35.82AutoAuto contrast35.82 contrast contrast35.82 contrast contrast PSNRImagesImages (dB)ImagesImages 35.8left)left) 35.8right)right) 35.84left)left) right) 35.86right) 35.82 PSNR(dB)PSNR(dB)PSNR(dB)PSNR(dB) 35.835.835.8 left)35.8left)left) left) 35.835.835.8 right) 35.8right)right)right) 35.8435.8435.84 left)35.84 left)left)left) 35.8635.8635.86 right)35.86 right)right)right) 35.8235.8235.82 35.82 ExtractedExtractedExtractedExtracted ExtractedExtractedExtractedExtracted PSNR(dB)ExtractedPSNR(dB)WatermAttackedWatermPSNR(dB)AttackedPSNR(dB)AttackedAttackedWatermWaterm 35.835.8 35.8 35.8 35.835.8 35.8 35.8 35.8435.8435.84 35.84 35.8635.8635.86 35.86 35.8235.8235.82 35.82 WatermarkAttackedAttackedAttackedAttackedAttacked PSNR(dB)PSNR(dB)AttackedWatermPSNR(dB)WatermPSNR(dB)WatermWaterm 35.835.835.8 35.8 35.835.835.8 35.8 35.8435.8435.84 35.84 35.8635.8635.86 35.86 35.8235.8235.82 35.82 ExtractedExtractedAttackedAttackedExtractedarkImagesAttackedExtractedarkAttackedImages ImagesImages arkImages ark arkImagesarkImagesImages ImagesImagesark Imagesark ExtractedExtractedNCExtractedImagesExtractedImagesImagesImagesImages Images 0.7316 0.7269 0.7411 0.7316 0.9838 WatermNCWatermNC WatermNC WatermNC 0.73160.73160.73160.7316 0.7269 0.7269 0.72690.7269 0.7411 0.74110.74110.7411 0.7316 0.73160.73160.7316 0.9838 0.98380.98380.9838 WatermNCWatermNC WatermNC WatermNC 0.73160.73160.73160.7316 0.72690.7269 0.72690.7269 0.7411 0.74110.74110.7411 0.73160.73160.73160.7316 0.9838 0.98380.98380.9838 arkark arkark PSNR(dB) PSNR(dB)PSNR(dB)PSNR(dB) 35.835.835.8 35.8 35.835.835.8 35.8 35.8435.8435.84 35.84 35.8635.8635.86 35.86 35.8235.8235.82 35.82 APSNR(dB)PSNR(dB)PSNR(dB) comparisonPSNR(dB)arkPSNR(dB)ark ark ark of quality35.835.835.835.8 35.8 of the watermark35.835.835.835.8 35.8 (NC values)35.8435.8435.8435.8435.84 for different35.8635.8635.8635.86 attacks35.86 is shown35.8235.8235.8235.8235.82 in Table 5. PSNR(dB)NCPSNR(dB)NC NC NC 0.73160.731635.80.731635.80.7316 0.72690.726935.80.726935.80.7269 0.74110.741135.8435.840.74110.7411 0.73160.731635.8635.860.73160.7316 0.9838Crop0.9838Crop35.8235.820.9838Crop0.9838 Crop The comparisonExtractedNCPSNR(dB)ExtractedNCPSNR(dB)ExtractedPSNR(dB) ExtractedNC NC is made0.73160.73160.731635.8 with0.731635.8 35.8 recent 0.72690.7269 CRT-based0.726935.80.726935.8 35.8 algorithms0.74110.74110.741135.8435.840.7411 35.84 by PatraHistogramHistogram0.73160.7316HistogramHistogram et0.731635.8635.86 al.0.7316 35.86 in [ 25 ,260.9838Crop0.9838].Crop It0.983835.82Crop35.82 can 0.9838 35.82Crop be seen SchemeScheme ExtractedExtractedExtractedSchemeExtractedSchemeExtracted Crop Crop Crop Crop NoiseNoiseNoise Noise Sharpen SharpenSharpenSharpen Invert InvertInvertInvert HistogramHistogramHistogramHistogram (Bottom(Bottom(Bottom(Bottom that thereExtracted ExtractedExtractedWatermExtractedExtractedWaterm isWaterm aWaterm signiﬁcant improvement in the quality of the watermark in invert, sharpen, histogram SchemeScheme SchemeWatermWatermSchemeWatermWaterm Waterm Crop CropCrop Crop NoiseNoiseNoise Noise Sharpen SharpenSharpenSharpen InvertInvert InvertInvert EqualizeEqualizeEqualizeEqualize (Bottom(Bottom(Bottom(Bottom Waterm EqualizeEqualizeEqualizeEqualize CropLeft)CropLeft)Left)Crop CropLeft) equalizationWatermWatermarkWatermarkWaterm andark ark cropping attacks using the proposed scheme. There HistogramHistogram isHistogramHistogram alsoa noticeable Left)Left) improvementLeft) Left) arkarkarkark ark CropCropCrop Crop in cropping,SchemeSchemeSchemeNCSchemearkNC NC arkark NC sharpening, arkarkark Crop CropCrop0.7316 Crop0.7316 0.7316 invert,0.7316 Noise Noise histogramNoise Noise 0.7269 0.7269Sharpen 0.7269Sharpen0.7269 equalizationSharpen Sharpen Invert0.7411 Invert0.7411 attacks0.7411Invert0.7411Invert compared HistogramHistogramHistogram0.73160.7316Histogram0.73160.7316 to Patra ( etBottom(Bottom al.0.9838(0.9838Bottom [(0.983826Bottom0.9838 ]. Further, SchemeSchemeSchemeScheme Crop CropCrop Crop NoiseNoiseNoise Noise Sharpen SharpenSharpenSharpen InvertInvert InvertInvert EqualizeEqualizeEqualizeEqualize (Bottom(Bottom(Bottom(Bottom thePatraPatra proposedPatra etPatra NCet al.NCNC al.NC et NC et schemeal. al. shows0.73160.73160.73160.73160.7316 some improvement0.72690.72690.72690.72690.7269 for all0.7411 the0.74110.74110.74110.7411 mentioned 0.7316 attacks0.73160.73160.73160.7316 compared Left)Left)0.98380.98380.9838 to0.9838Left) 0.9838Left) the scheme Patra Patra Patra etNC Patraet NCNCal. al.NCet NC etal. al. 0.73160.73160.73160.73160.7316 0.72690.72690.72690.72690.7269 0.74110.74110.74110.74110.7411 EqualizeEqualize0.7316Equalize0.73160.7316Equalize0.73160.7316 0.98380.98380.98380.98380.9838 by Patra [39] [39] et al.[39] [39] [25 ]. Left)Left)Left) Left) [39][39] [39] [39] CropCropCropCrop PatraPatraPatra etPatra et al. al. et et al. al. HistogramHistogramHistogramHistogram CropCropCropCropCrop PatraPatraNCSchemePatraNCScheme et SchemePatra et NCScheme al. NCal. et etal. al. 0.7650 0.7650Table Crop0.7650Crop0.7650Crop Crop5. NC 0.9970 0.9970 comparisonNoise0.9970Noise0.9970Noise Noise 0.3798 of0.3798Sharpen extractedSharpenSharpen0.3798Sharpen0.3798 watermark0.1563 0.1563 InvertInvert0.1563InvertInvert0.1563 Invert after 0.1344Histogram common0.1344HistogramHistogramHistogram0.1344Histogram0.1344 attacks. 0.75290.7529(BottomCrop(BottomCrop0.7529(Crop(Bottom(0.7529Crop BottomCrop SchemeNCNC[39]SchemeScheme[39]Scheme NCScheme [39]NC [39] 0.7650 0.7650Crop0.7650CropCropCrop0.7650 Crop 0.9970 0.9970NoiseNoise0.9970NoiseNoise0.9970 Noise 0.3798 Sharpen0.3798SharpenSharpenSharpen0.3798Sharpen0.3798 0.1563 0.1563 InvertInvertInvert0.1563InvertInvert0.1563 Invert Histogram0.1344Equalize0.1344HistogramHistogramEqualizeHistogramEqualizeHistogram0.1344Equalize0.1344 0.75290.7529(Bottom((BottomBottom(0.7529(Bottom(0.7529Bottom SchemeSchemeSchemeSchemeScheme CropCropCropCropCrop NoiseNoiseNoiseNoiseNoise Sharpen SharpenSharpenSharpenSharpen InvertInvertInvertInvertInvertInvert EqualizeEqualizeEqualizeEqualizeEqualize (Bottom((Left)BottomBottom((Left)Bottom(Left)BottomLeft) [39][39] [39] [39] Equalize PatraPatraPatra etPatra et al. al. et et al. al. EqualizeEqualizeEqualizeHistogramEqualize Left)Left)Left)CropLeft)Left) PatraPatraSchemeNCPatraNC et Patra et NC al. al. NCet etal. al.0.7650 0.7650 Crop0.76500.7650 0.9970 0.99700.9970 Noise0.9970 0.3798 0.37980.3798 Sharpen0.3798 0.1563 0.15630.15630.1563 Invert 0.13440.13440.13440.1344 0.75290.7529Left)Left)0.7529Left)0.7529Left) Left) NCNC[25][25] NC [25]NC [25] 0.76500.76500.76500.7650 0.9970 0.99700.99700.9970 0.3798 0.37980.37980.3798 0.1563 0.15630.15630.1563 0.13440.13440.1344Equalize0.1344 0.75290.7529(Bottom0.75290.7529 Left) PatraPatra[25]Patra[25]Patra et [25]et al. et [25]al.et al. al. PatraPatraPatraPatraPatra et et etal. et al. al.et al. al. PatraPatraPatraPatra etPatra et etal. al. al. et et al. al. PatraPatraPatraNCPatraPatraPatraNC Patraet Patra et et[39] NC al. [39]etNCal. etet [39] [39] et et al. [39]al.etal. al.al. al.0.7097 0.7097 0.70970.7097 0.7012 0.70120.70120.7012 0.4997 0.49970.49970.4997 0.9413 0.94130.94130.9413 0.53710.53710.53710.5371 0.74170.74170.74170.7417 NCNC[25][25] [39]NC [39] [39][25]NC [39][39][25] [39] 0.7097 0.70970.70970.7097 0.7012 0.7012 0.70120.7012 0.4997 0.4997 0.49970.4997 0.9413 0.9413 0.94130.9413 0.53710.53710.53710.5371 0.74170.74170.74170.7417 [39[39]] [39][39][39][39] [39] [25][25] [25] [25] NCNCNC NC 0.76500.76500.76500.7650 0.9970 0.99700.99700.9970 0.3798 0.37980.37980.3798 0.15630.15630.15630.1563 0.13440.13440.13440.1344 0.75290.75290.75290.7529 ProposedProposedNCNCProposedNCNCProposed NCNCNC NC NC NC 0.7097 0.70970.7650 0.76500.76500.76500.70970.76500.7097 0.7650 0.7012 0.70120.99700.99700.99700.7012 0.99700.99700.7012 0.9970 0.4997 0.49970.37980.37980.37980.49970.37980.4997 0.3798 0.3798 0.9413 0.94130.15630.15630.15630.94130.15630.9413 0.1563 0.1563 0.53710.53710.13440.13440.13440.53710.13440.53710.1344 0.1344 0.74170.74170.75290.75290.75290.7417 0.75290.75290.7417 0.7529 ProposedProposedNCNCProposedNC NCProposedNCNC NC NC 0.70970.7097 0.76500.76500.76500.70970.76500.76500.7097 0.7012 0.70120.99700.99700.99700.70120.99700.99700.7012 0.4997 0.49970.37980.37980.37980.49970.37980.37980.4997 0.9413 0.94130.15630.15630.15630.94130.15630.15630.9413 0.53710.53710.13440.13440.13440.53710.13440.13440.5371 0.74170.74170.75290.75290.75290.74170.75290.75290.7417 PatraPatraPatraPatra et et al. et al.et al. al. PatraPatraPatraPatraPatra et et etal. et al. al.et al. al. ProposedProposedPatraPatraNCPatraPatraNCProposedPatraPatraProposed et[25]NC [25] etNCal. et al. [25] [25] et et al.[25]al.et al.al. al.0.7158 0.7158 0.71580.7158 0.7208 0.72080.72080.7208 0.5034 0.50340.50340.5034 0.9771 0.97710.97710.9771 0.54150.54150.54150.5415 0.74110.74110.74110.7411 ProposedProposedNCNC[Proposed25 Proposed[25]]NC [25][25] NC[25][25] [25] 0.7158 0.7158 0.71580.7158 0.7208 0.7208 0.72080.7208 0.5034 0.5034 0.50340.5034 0.9771 0.9771 0.97710.9771 0.54150.54150.54150.5415 0.74110.74110.74110.7411 [25][25][25][25][25] [25] NCNCNC NC 0.70970.70970.70970.7097 0.70120.70120.70120.7012 0.4997 0.49970.49970.4997 0.94130.94130.94130.9413 0.53710.53710.53710.5371 0.74170.74170.74170.7417 NCNCNCNC NCNCNC NC NCNC 0.71580.71580.7097 0.70970.70970.70970.71580.70970.7158 0.7097 0.7208 0.72080.70120.70120.70120.7208 0.70120.70120.7208 0.7012 0.5034 0.50340.49970.49970.49970.50340.49970.5034 0.4997 0.4997 0.9771 0.97710.94130.94130.94130.97710.94130.9771 0.9413 0.9413 0.54150.54150.53710.53710.53710.54150.53710.54150.5371 0.5371 0.74110.74110.74170.74170.74170.7411 0.74170.74170.7411 0.7417 NCNCNC NCNC 0.70970.70970.70970.70970.7097 0.70120.70120.70120.70120.7012 0.49970.49970.49970.49970.4997 0.94130.94130.94130.94130.9413 0.53710.53710.53710.53710.5371 0.74170.74170.74170.74170.7417 NCNC NC NC 0.71580.71580.71580.7158 0.7208 0.72080.72080.7208 0.5034 0.50340.50340.50341 1 1 10.9771 0.97710.97710.9771 0.54150.54150.54150.5415 0.74110.74110.74110.7411 1 1 1 1 Proposed Proposed ProposedProposedProposed ProposedProposedProposed ProposedProposed ProposedProposedProposedProposedProposed 1 1 1 1 NC 0.7158 0.7208 0.5034 0.9771 0.5415 0.7411 NCNCNC NC 0.71580.71580.71580.7158 0.72080.72080.72080.7208 0.50340.50340.50340.50341 1 1 1 0.97710.97710.97710.9771 0.54150.54150.54150.5415 0.74110.74110.74110.7411 NCNCNCNC NC 0.71580.71580.71580.71580.7158 0.72080.72080.72080.72080.7208 0.50340.50340.50340.50340.5034 0.97710.97710.97710.97710.9771 0.54150.54150.54150.54150.5415 0.74110.74110.74110.74110.7411 NCNCNC NCNC 0.71580.71580.71580.71580.7158 0.72080.72080.72080.72080.7208 0.50340.50340.50340.50340.5034 0.97710.97710.97710.97710.9771 0.54150.54150.54150.54150.5415 0.74110.74110.74110.74110.7411

1 1 1 1 1 11 1 1 1 1 1 1 1

Future Internet 2017, 9, 33 14 of 16

5. Conclusions A new approach for authentication and copyright protection based on DCT and listless SPIHT is proposed in this paper. The proposed method exhibit improved security and robustness due to incorporation of CRT for modifying the coefficients. The coefficient to be modified is selected randomly for every block. The average PSNR performance for compression is better compared to other CRT-based algorithms. Satisfactory TAF and NC values for watermark are obtained on a compression ratio up to 25:1 in most of the images, which is not possible in other schemes. Some major image attacks such as brightening, sharpening, noise, cropping, and inversion in the proposed scheme exhibit better resistance to the DCT-based CRT scheme. The overall complexity of the proposed scheme is less complex because CRT involves only modular operations for computation along with low-complexity listless SPIHT. Further research will involve implementing the proposed scheme on video watermarking.

Acknowledgments: We would like to thank anonymous reviewers for their valuable suggestions and comments to improve the quality of the manuscript. Author Contributions: J. L. Divya Shivani analysed the data, contributed literature survey and assisted in simulation work; Ranjan K. Senapati conceived the idea, designed and performed the experiment. Conﬂicts of Interest: The authors declare no conﬂicts of interest.

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