Dependable AI

Papers

  1. Towards Evaluating the Robustness of Neural Networks – Nicholas Carlini, David Wagner, University of California, Berkeley – Link
  2. Defense against Universal Adversarial Perturbations – Naveed Akhtar, Jian Liu, Ajmal Mian – Link
  3. Local Gradients Smoothing: Defense against localized adversarial attacks – Muzammal Naseer, Salman H. Khan – Link
  4. Sparse and Imperceivable Adversarial Attacks – Francesco Croce, Matthias Hein – Link
  5. Interpretable and Fine-Grained Visual Explanations for
    Convolutional Neural Networks – Jorg Wagner, Jan Mathias Kohler, Tobias Gindele, Leon Hetzel – Link
  6. Interpreting Black Box Models via Hypothesis Testing – Collin Burns, Jesse Thomason, Wesley Tansey – Link
  7. PeerNets: Exploiting Peer Wisdom Against Adversarial Attacks – Jan Svoboda, Jonathan Masci, Federico Monti, Michael M. Bronstein, Leonidas Guibas – Link
  8. Adversarial Defense via Learning to Generate Diverse Attacks – Yunseok Jang, Tianchen Zhao, Seunghoon Hong, Honglak Lee – Link
  9. Efficient Decision-based Black-box Adversarial Attacks on Face Recognition – Yinpeng Dong, Hang Su, Baoyuan Wu, Zhifeng Li, Wei Liu, Tong Zhang, Jun Zhu – Link
  10. There and Back Again: Revisiting Backpropagation Saliency Methods – Sylvestre-Alvise Rebuffi, Ruth Fong, Xu Ji, Andrea Vedaldi – Link
  11. Adversarial camera stickers: A physical camera-based attack on deep learning systems – Juncheng B. Li, Frank R. Schmidt, J. Zico Kolter – Link
  12. Are Labels Required for Improving Adversarial Robustness? – Jonathan Uesato, Jean-Baptiste Alayrac, Po-Sen Huang, Robert Stanforth Alhussein Fawzi Pushmeet Kohli – Link
  13. Simple Black-box Adversarial Attacks – Chuan Guo, Jacob R. Gardner, Yurong You, Andrew Gordon Wilson, Kilian Q. Weinberger – Link
  14. Distillation as a Defense to Adversarial Perturbations against Deep Neural Networks – Nicolas Papernot, Patrick McDaniel, Xi Wu, Somesh Jha, and Ananthram Swami – Link
  15. Practical No-box Adversarial Attacks against DNNs – Qizhang Li, Yiwen Guo, Hao Chen – Link
  16. VISUAL EXPLANATION BY INTERPRETATION: IMPROVING VISUAL FEEDBACK CAPABILITIES OF DEEP NEURAL NETWORKS РJos̩ Oramas M, Kaili Wang, Tinne Tuytelaars РLink
  17. DNDNet: Reconfiguring CNN for Adversarial Robustness – Akhil Goel, Akshay Agarwal, Mayank Vatsa, Richa Singh, and Nalini K. Ratha – Link
  18. DECISION-BASED ADVERSARIAL ATTACKS: RELIABLE ATTACKS AGAINST BLACK-BOX MACHINE LEARNING MODELS – Wieland Brendel, Jonas Rauber & Matthias Bethge – Link
  19. A Survey on Bias and Fairness in Machine Learning – Link
  20. Balanced Datasets Are Not Enough: Estimating and Mitigating Gender Bias in Deep Image Representations – Link
  21. Bias in Data-driven AI Systems – An Introductory Survey – Link

Others

  1. Adversarial Robustness Toolbox (ART) v1.5 – Link1 Link2

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