This repo contains notes and short summaries of some DNN security and Software Engineering & AI related papers I come across.
- Leveraging Unlabeled Data for Watermark Removal of Deep Neural Networks (ICML 2019): [Paper] [Notes]
- Turning Your Weakness Into a Strength: Watermarking Deep Neural Networks by Backdooring (usenix 2018): [Paper] [Code] [Blog] [Notes] [Citation]
- TEXTBUGGER: Generating Adversarial Text Against Real-world Applications (NDSS 2019): [Paper]
- Text processing like humans do: Visually attacking and shielding NLP systems (NAACL 2019): [Paper]
- Black-box generation of adversarial text sequences to evade deep learning classifiers (S&P 2018): [Paper]
- Hotflip: White-box adversarial examples for text classification (ACL 2018): [Paper]
- Generating natural language adversarial examples (EMNLP 2018): [Paper]
- Deep text classification can be fooled (IJCAI 2018): [Paper]
- Generating Fluent Adversarial Examples for Natural Languages (ACL 2019): [Paper]
- Generating natural language adversarial examples through probability weighted word saliency (ACL 2019): [Paper]
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Neural Cleanse: Identifying and Mitigating Backdoor Attacks in Neural Networks (S&P 2019): [Paper] [Code] [Notes] [Citation]
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STRIP: A Defence Against Trojan Attacks on Deep Neural Networks (2019 ACSAC): [Paper] [Code] [Notes] [Citation]
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Design and Evaluation of a Multi-Domain Trojan Detection Method on Deep Neural Networks (2019): [Paper] [Notes] [Citation]
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DeepInspect: A Black-box Trojan Detection and Mitigation Framework for Deep Neural Networks (IJCAI 2019): [Paper] [Notes] [Citation]
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ABS: Scanning Neural Networks for Back-doors by Artificial Brain Stimulation (CCS 2019): [Paper] [Citation]
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TABOR: A Highly Accurate Approach to Inspecting and Restoring Trojan Backdoors in AI Systems (2019): [Paper] [Notes] [Citation]
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Detecting Backdoor Attacks on Deep Neural Networks by Activation Clustering (2019 AAAI): [Paper] [Code] [Notes] [Citation]
- DEEPSEC: deciding equivalence properties in security protocols theory and practice (S&P 2018): [Paper]
- Guiding deep learning system testing using surprise adequacy (ICSE 2019): [Paper]
- DeepGauge: Multi-granularity testing criteria for deep learning systems (ASE 2018): [Paper]
- DeepXplore: Automated whitebox testing of deep learning systems (SOSP 2017): [Paper]
- DeepTest: Automated testing of deep-neural-network-driven autonomous cars (ICSE 2018): [Paper]
- DeepHunter: a coverage-guided fuzz testing framework for deep neural networks (ISSTA 2019): [Paper]
- There is limited correlation between coverage and robustness for deep neural networks (ASE 2019): [Paper]