Timezone: »
Poster
Second-Order Provable Defenses against Adversarial Attacks
Sahil Singla · Soheil Feizi
Wed Jul 15 05:00 AM -- 05:45 AM & Wed Jul 15 04:00 PM -- 04:45 PM (PDT) @ Virtual
A robustness certificate against adversarial examples is the minimum distance of a given input to the decision boundary of the classifier (or its lower bound). For {\it any} perturbation of the input with a magnitude smaller than the certificate value, the classification output will provably remain unchanged. Computing exact robustness certificates for neural networks is difficult in general since it requires solving a non-convex optimization. In this paper, we provide computationally-efficient robustness certificates for neural networks with differentiable activation functions in two steps. First, we show that if the eigenvalues of the Hessian of the network (curvatures of the network) are bounded (globally or locally), we can compute a robustness certificate in the $l_2$ norm efficiently using convex optimization. Second, we derive a computationally-efficient differentiable upper bound on the curvature of a deep network. We also use the curvature bound as a regularization term during the training of the network to boost its certified robustness. Putting these results together leads to our proposed {\bf C}urvature-based {\bf R}obustness {\bf C}ertificate (CRC) and {\bf C}urvature-based {\bf R}obust {\bf T}raining (CRT). Our numerical results show that CRT leads to significantly higher certified robust accuracy compared to interval-bound propagation based training.
Author Information
Sahil Singla (University of Maryland)
Soheil Feizi (University of Maryland)
More from the Same Authors
-
2022 : Towards Better Understanding of Self-Supervised Representations »
Neha Mukund Kalibhat · Kanika Narang · Hamed Firooz · Maziar Sanjabi · Soheil Feizi -
2022 : Improved Certified Defenses against Data Poisoning with (Deterministic) Finite Aggregation »
Wenxiao Wang · Alexander Levine · Soheil Feizi -
2022 : Certifiably Robust Multi-Agent Reinforcement Learning against Adversarial Communication »
Yanchao Sun · Ruijie Zheng · Parisa Hassanzadeh · Yongyuan Liang · Soheil Feizi · Sumitra Ganesh · Furong Huang -
2023 Poster: Run-off Election: Improved Provable Defense against Data Poisoning Attacks »
Keivan Rezaei · Kiarash Banihashem · Atoosa Malemir Chegini · Soheil Feizi -
2023 Poster: Identifying Interpretable Subspaces in Image Representations »
Neha Mukund Kalibhat · Shweta Bhardwaj · C. Bayan Bruss · Hamed Firooz · Maziar Sanjabi · Soheil Feizi -
2023 Poster: Text-To-Concept (and Back) via Cross-Model Alignment »
Mazda Moayeri · Keivan Rezaei · Maziar Sanjabi · Soheil Feizi -
2022 : Panel discussion »
Steffen Schneider · Aleksander Madry · Alexei Efros · Chelsea Finn · Soheil Feizi -
2022 : Improved Certified Defenses against Data Poisoning with (Deterministic) Finite Aggregation »
Wenxiao Wang · Alexander Levine · Soheil Feizi -
2022 : Toward Efficient Robust Training against Union of Lp Threat Models »
Gaurang Sriramanan · Maharshi Gor · Soheil Feizi -
2022 Poster: Improved Certified Defenses against Data Poisoning with (Deterministic) Finite Aggregation »
Wenxiao Wang · Alexander Levine · Soheil Feizi -
2022 Poster: FOCUS: Familiar Objects in Common and Uncommon Settings »
Priyatham Kattakinda · Soheil Feizi -
2022 Spotlight: Improved Certified Defenses against Data Poisoning with (Deterministic) Finite Aggregation »
Wenxiao Wang · Alexander Levine · Soheil Feizi -
2022 Spotlight: FOCUS: Familiar Objects in Common and Uncommon Settings »
Priyatham Kattakinda · Soheil Feizi -
2021 : Invited Talk 6: T​owards Understanding Foundations of Robust Learning »
Soheil Feizi -
2021 Poster: Improved, Deterministic Smoothing for L_1 Certified Robustness »
Alexander Levine · Soheil Feizi -
2021 Poster: Skew Orthogonal Convolutions »
Sahil Singla · Soheil Feizi -
2021 Spotlight: Skew Orthogonal Convolutions »
Sahil Singla · Soheil Feizi -
2021 Oral: Improved, Deterministic Smoothing for L_1 Certified Robustness »
Alexander Levine · Soheil Feizi -
2020 Poster: Curse of Dimensionality on Randomized Smoothing for Certifiable Robustness »
Aounon Kumar · Alexander Levine · Tom Goldstein · Soheil Feizi -
2020 Poster: On Second-Order Group Influence Functions for Black-Box Predictions »
Samyadeep Basu · Xuchen You · Soheil Feizi -
2019 Poster: Understanding Impacts of High-Order Loss Approximations and Features in Deep Learning Interpretation »
Sahil Singla · Eric Wallace · Shi Feng · Soheil Feizi -
2019 Oral: Understanding Impacts of High-Order Loss Approximations and Features in Deep Learning Interpretation »
Sahil Singla · Eric Wallace · Shi Feng · Soheil Feizi -
2019 Poster: Entropic GANs meet VAEs: A Statistical Approach to Compute Sample Likelihoods in GANs »
Yogesh Balaji · Hamed Hassani · Rama Chellappa · Soheil Feizi -
2019 Oral: Entropic GANs meet VAEs: A Statistical Approach to Compute Sample Likelihoods in GANs »
Yogesh Balaji · Hamed Hassani · Rama Chellappa · Soheil Feizi