Boosting Adversarial Robustness using Feature Level Stochastic Smoothing
Advances in adversarial defenses have led to a significant improvement in the robustness of Deep Neural Networks. However, the robust accuracy of present state-ofthe-art defenses is far from the requirements in critical applications such as robotics and autonomous navigation systems. Further, in practical use cases, network prediction alone might not suffice, and assignment of a confidence value for the prediction can prove crucial. In this work, we propose a generic method for introducing stochasticity in the network predictions, and utilize this for smoothing decision boundaries and rejecting low confidence predictions, thereby boosting the robustness on accepted samples. The proposed Feature Level Stochastic Smoothing based classification also results in a boost in robustness without rejection over existing adversarial training methods. Finally, we combine the proposed method with adversarial detection methods, to achieve the benefits of both approaches.
Code (1)
Tasks
Adversarial RobustnessAutonomous NavigationSimilar Papers 제목 키워드 기반
A Multiclass Boosting Framework for Achieving Fast and Provable Adversarial Robustness
Alongside the well-publicized accomplishments of deep neural networks there has emerged an apparent bug in their success on tasks such as object recognition: with deep models trained using vanilla methods, input images c…
Adversarial RobustnessObject RecognitionImproving the Behaviour of Vision Transformers with Token-consistent Stochastic Layers
We introduce token-consistent stochastic layers in vision transformers, without causing any severe drop in performance. The added stochasticity improves network calibration, robustness and strengthens privacy. We use lin…
Adversarial RobustnessTransfer LearningCertified vs. Empirical Adversarial Robust-ness via Hybrid Convolutions with Attention Stochasticity
We introduce Hybrid Convolutions with Attention Stochasticity (HyCAS), an adversarial defense that narrows the long-standing gap between provable robustness under L2 certificates and empirical robustness against strong L…
Adversarial RobustnessAdversarial DefenseRobust Boosting Forests with Richer Deep Feature Hierarchy
We propose a robust variant of boosting forest to the various adversarial defense methods, and apply it to enhance the robustness of the deep neural network. We retain the deep network architecture, weights, and middle l…
Adversarial DefenseFace ModelAFD: Mitigating Feature Gap for Adversarial Robustness by Feature Disentanglement
Adversarial fine-tuning methods enhance adversarial robustness via fine-tuning the pre-trained model in an adversarial training manner. However, we identify that some specific latent features of adversarial samples are c…
Adversarial RobustnessDisentanglement