Papers you can read

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• Compression Aware Certified Training; Changming, Xu, Gagandeep Singh
• Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples; Anish Athalye, Nicholas Carlini, David Wagner
• On the Interaction of Compressibility and Adversarial Robustness; Barsbey et al.
• A Comprehensive Review of Adversarial Attacks and Defense Strategies in Deep Neural Networks; Abomakhelb et al.
• Reliable Evaluation of Adversarial Robustness with an Ensemble of Diverse Parameter-free Attacks; Francesco Croce, Matthias Hein
• Explaining And Harnessing Adversarial Examples, Ian J. Goodfellow, Jonathon Shlens & Christian Szegedy
• On Evaluating Adversarial Robustness, Carlini et al.
• Towards Deep Learning Models Resistant to Adversarial Attacks, Madry et al.
• Adversarial Pruning: A Survey and Benchmark of Pruning Methods for Adversarial Robustness, Piras et al.
• Certified Adversarial Robustness via Randomized Smoothing, Cohen et al.
• Provably Robust Deep Learning via Adversarially Trained Smoothed Classifiers, Salman et al.
• Provable Defenses against Adversarial Examples via the Convex Outer Adversarial Polytope, Wong et al.
• Scaling provable adversarial defenses, Wong et al.
• Synthesizing Robust Adversarial Examples, Athalye et al.
• DEFENSIVE QUANTIZATION: WHEN EFFICIENCY MEETS ROBUSTNESS, Lin et al.
• IMPROVING ADVERSARIAL ROBUSTNESS REQUIRES REVISITING MISCLASSIFIED EXAMPLES, Wang et al.
• How Does Batch Normalization Help Optimization?, Santurkar et al.
• FAST IS BETTER THAN FREE: REVISITING ADVERSARIAL TRAINING, Wong et al.
• Adversarial Examples Are Not Bugs, They Are Features, Ilyas et al.
• Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples, Athalye et al.
• Quantization Aware Attack: Enhancing Transferable Adversarial Attacks by Model Quantization, Yang.
• RobustMQ: Benchmarking Robustness of Quantized Models, Xiao, Zhang, Liu, Qin (2023).
• A survey on adversarial machine learning: Attacks, defenses, real-world applications, and future research directions, Qiao et al.
• Li et al., Investigating the Impact of Quantization on Adversarial Robustness
• Vincent et al., TinyML Student Paper
• West et al., Towards quantum enhanced adversarial robustness in machine learning
• Osama et al., Chaotic neural network quantization and its robustness against adversarial attacks
• Vassilev et al., NIST Adversarial Machine Learning
• Dong, Y., Liao, F., Pang, T., et al. (2018). Boosting Adversarial Attacks with Momentum. CVPR 2018. (MIM)
• Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., & Frossard, P. (2017). Universal Adversarial Perturbations. CVPR 2017. (UAP)
• Brendel, W., Rauber, J., & Bethge, M. (2018). Decision-Based Adversarial Attacks: Reliable Attacks Against Black-Box Machine Learning. ICLR 2018. (Boundary Attack)
• Papernot, N., McDaniel, P., Goodfellow, I., Jha, S., Celik, Z. B., & Swami, A. (2017). Practical Black-Box Attacks against Machine Learning. Proceedings of the 2017 ACM Asia Conference on Computer and Communications Security, 506–519.

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Other implementations similar to ours on GitHub:

• https://github.com/git-disl/GTAttackPod

[NightlyTwo58]

Source List Desp.xlsx