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Artificial Intelligence and Machine Learning General Definitions and Fraud/AML Applications Artificial Intelligence and Machine Learning general definitions and fraud/AML applications Frankfurt, April 2019 Group Financial Crime Eric WAGNER Agenda „difference between artificial intelligence and machine learning?“ 1 Current progress? Different types of machine 2 learning 3 approach 4 examples 5 Erste Group applications “By far, the greatest danger of Artificial Intelligence is that and AML synergies people conclude too early that they understand it.” Eliezer Yudkowsky1 6 challenges “Nobody phrases it this way, but I think that artificial intelligence is almost a humanities discipline. It's really an attempt to understand human intelligence and human 7 cognition.” conclusio 2 Sebastian Thrun 8 1 US researche and author. Decision theory and longterm social and philosophical impacts of artificial intelligence 2 German computer scientist and robotic specialist. Former professor for artificial intelligence at Stanford University and vice president of Google. Unfortunately no common definition, but converging opinions and papers EXEMPLARY General Artificial Intelligence Specific Artificial Intelligence „Humanity“ „nothing less than build a machine, a robot • empathy / mood • pass through its childhood, • opinion (having / reasoning) • learns languages like a child, • culture / faith / love • gains knowledge about the world by observing it with its own organs and „visual nature“ • ultimately contemplates about the whole • “human look” human knowledge and intellectual world” • Body movements and -functions • Mimic/gesture Joseph Weizenbaum, MIT AI Laboratory „interaction“ • perception (5+ senses) „Turing Test: a test person tries to identify if • intervention the unknown counterpart is human or a • context (spacial / logic) machine based on interactions [originally via keyboard and screen only] …„ „problem solving“ • recognize/deviate problem (situation) Alan Turing, 1950 • problem resolution (general and specific) • (Machine) Learning ca. 20 years to go for comprehensive artificial intelligence, but in specific areas „superhuman“ performance have already been achieved. Estimation current status AI/ML No „average“ „Humanity“ performance human • empathy / mood • opinion (having / reasoning) • culture / faith / love „visual nature“ • “human look” • Body movements and -functions • Mimic/gesture „interaction“ • perception (5+ senses) • intervention • context (spacial / logic) „problem solving“ • recognize/deviate problem (situation) general specific • problem resolution (general and specific) • (Machine) Learning further focus In general Machine Learning can be separated in three categories … Supervised Learning Unsupervised Learning Reinforcement Learning regression (cont. output) clustering status optimization by rewards classification (discrete output) outlier/anomaly detection Types • labeled training data • no labeled training data • decision process • direct feedback • no feedback • delayed feedback via “reward • predict result • find the “hidden” structure system” • agent evaluates (new) status and initiates actions to further optimize Characteristics own status • recognition: handwriting, speech and • system diagnosis • autonomous cars, robots, elevators, pictures • security-/event detection etc. • recommendation: Spamfilter, Online- • analysis: social network, astronomy • (computer-)games (AlphaGo) ads, recommender systems • market segmentation • marketing strategy optimization • analysis: brainsignals, genes, share Examples prices, weather, … … which can be realized either by statistical machine learning methods (can be re-calculated) or neural networks (cannot be re-cacluated). EXEMPLARY Supervised Learning Unsupervised Learning • Lineare/Ridge Regression1 • LVQ - Linear Vector Quantization (videocodecs: Apple Quicktime and audiocodecs: DTS, Ogg Vorbis) • K-Nearest Neighbor (regression/classification) • K-Means (fast, but heuristic for market segmentation, computer • Decision Trees2 (regression/classification) vision, geo statistics, agriculture) • Logistic Regression3 • HBOS - Histogram Based Outlier Scoring (fraud, structural learning • SVM – Support Vector Machine (binary classification) defects) • Naive Bayes (document-/text classification) • One-Class SVM Statistical machine (learning via „back propagation“) • NN (perceptron, RBM, autoencoder) Clustering • Deep NN (recurrent/LSTM, convolutional, • Kohonen SOM (Self-Organizing Map) • generative adversarial) • Neural Gas • DeepQ/Hierarchical Reinforcement NN neural networks Reinforcement Learning • rarely used an autonomous learning-method, • but as specific evaluation-algorithm of learning successes/-failures • Within supervised/unsupervised learning methods 1 avoidance of overfitting: recognize connections, separation of signal and noise incl. error estimation 2 Decision Trees, incl. Random Forest, Monte-Carlo Decision Tree etc. can be either regression (numerical output) or classification (discrete output) 3 binary problems and analysis of context probability with multiple features: market research, impact analysis Numerous successful Machine Learning implementations (realtime and faster/better than human) … (1/2) Autonomous vehicles Picture recognition and description as well (NVIDIA DRIVE PX) as context queries Object recognition and classification Object recognition and description https://www.youtube.com/embed/0rc4RqYLtEU?end=125&fs= 0&modestbranding=1&showinfo=0&autoplay=1 Computer model generation of environment Natural language question analysis and -answering for object recognition incl. context/relation https://www.youtube.com/embed/PjH_1hEoIDs?end=100&fs= 0&modestbranding=1&showinfo=0&autoplay=1 Numerous successful Machine Learning implementations (realtime and faster/better than human) … (2/2) Autonomous learning of games and Medical Diagnostics simulation of intuitive moves Autonomous learning of computer games (2014) Deep Learning via Computer Vision leads to Deep Q-Network (DQN) + Access to Care and Diagnostic Accuracy Reinforcement Learning https://www.youtube.com/embed/n_- xKr3vF3M?fs=0&modestbranding=1&showinfo=0&autoplay=1 Simulation of „intuitive“ moves (AlphaGo 2016) Monte Carlo Tree Search and for each Branch/Leaf Evaluation … 4 CNNs (3 Policy + 1 Value CNN) IEEE Spectrum … and advanced Machine Learning based approaches for fraudulent use (1/5) Spoofing / Disguising / Phishing everything Spoofcard.com: SNAP_R | Black Hat | blackhat.com • Disguise Caller ID • Disguise voice (sound as man SNAP_R (Social Network Automated Phishing with Reconnaissance) or woman) and add background AI came out in an experiment to get more Twitter users to click malicious links sounds than a human competitor by • Call straight to voicemail • studied how Twitter users behave, • Send spoof texts • then designed and implemented its own phishing bait. https://www.spoofcard.com • The results of the experiment showed that the artificial hacker was able to compose and distribute more phishing tweets, and with a more substantial conversion rate. Authentication Factor Description Key Vulnerabilities Security Key A compact device that Can be hacked/manipulated contains a secure IC chip Ownership which leverages public-key infrastructure Fingerprint Compares fingerprint on Usage of Master-Fingerprint Scanning record with new scans captured optically or electrically Vein Scanning Compares veins on record Can be „stolen“ and forged/faked with new scans captured Inherence optically 3D Facial Compares 3D characteristics 3D-print (mostly Android and Win-Hello, iOS less vulnerable) Recognition of a face on record with new scans captured optically 10 … and advanced Machine Learning based approaches for fraudulent use (2/5) Fake Sound based on written Text: Tacotron 2 - … and even trained to sound like an existing human Generating Human-like Speech from Text (VoCo/Lyrebird.ai) https://google.github.io/tacotron/publications/tacotron2/index.html https://arxiv.org/abs/1711.10433https://arxiv.org/abs/1711.10433https://arxiv.org/abs/ 1711.10433 Generate human-like speech from text using neural networks trained using only speech examples and corresponding text transcripts. Sequence of features (i.e. an 80-dimensional audio spectrogram with frames computed every 12.5 milliseconds) encoding an audio, capture not only • pronunciation of words, but also • various subtleties of human speech, including volume, speed and intonation. Deepminds WaveNet (Parallel WaveNet: Fast High-Fidelity Speech Synthesis), deployed already in Google Assistant Fake Video based on Audio (in Realtime): Adapting Lip-Sync from Audio https://grail.cs.washington.edu/projects/AudioToObama/ Input: Given audio of President Barack Obama Output: Synthesize a photorealistic video of Obama speaking with accurate lip sync, composited into a target video clip. Training: Trained on many hours of his weekly address footage, • a recurrent neural network learns the mapping from raw audio features to mouth shapes. • Given the mouth shape at each time instant, high quality mouth texture was synthesized and • composite with proper 3D pose matching to change what he appears to be saying in a target video to match the input audio track. https://www.youtube.com/embed/9Yq67CjDqvw?start=45&e nd=62&autoplay=1 11 … and advanced Machine Learning based approaches for fraudulent use (3/5) Fake Video based on swapped face (NOT Realtime): DeepFake (IN Realtime): FaceSwap https://www.fakeapp.org/ various implementations, but not fully convincing ... yet https://hackernoon.com/exploring-deepfakes- 20c9947c22d9 http://faceswaplive.com/ https://github.com/arturoc/FaceSubstit
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