DeepAI AI Chat
Log In Sign Up

Detection Defense Against Adversarial Attacks with Saliency Map

by   Dengpan Ye, et al.

It is well established that neural networks are vulnerable to adversarial examples, which are almost imperceptible on human vision and can cause the deep models misbehave. Such phenomenon may lead to severely inestimable consequences in the safety and security critical applications. Existing defenses are trend to harden the robustness of models against adversarial attacks, e.g., adversarial training technology. However, these are usually intractable to implement due to the high cost of re-training and the cumbersome operations of altering the model architecture or parameters. In this paper, we discuss the saliency map method from the view of enhancing model interpretability, it is similar to introducing the mechanism of the attention to the model, so as to comprehend the progress of object identification by the deep networks. We then propose a novel method combined with additional noises and utilize the inconsistency strategy to detect adversarial examples. Our experimental results of some representative adversarial attacks on common datasets including ImageNet and popular models show that our method can detect all the attacks with high detection success rate effectively. We compare it with the existing state-of-the-art technique, and the experiments indicate that our method is more general.


page 5

page 6


MUTEN: Boosting Gradient-Based Adversarial Attacks via Mutant-Based Ensembles

Deep Neural Networks (DNNs) are vulnerable to adversarial examples, whic...

Adversarial Attacks are Reversible with Natural Supervision

We find that images contain intrinsic structure that enables the reversa...

Adversarial Training for Free!

Adversarial training, in which a network is trained on adversarial examp...

Online Alternate Generator against Adversarial Attacks

The field of computer vision has witnessed phenomenal progress in recent...

Identifying Untrustworthy Predictions in Neural Networks by Geometric Gradient Analysis

The susceptibility of deep neural networks to untrustworthy predictions,...

Gotta Catch 'Em All: Using Concealed Trapdoors to Detect Adversarial Attacks on Neural Networks

Deep neural networks are vulnerable to adversarial attacks. Numerous eff...

Trace and Detect Adversarial Attacks on CNNs using Feature Response Maps

The existence of adversarial attacks on convolutional neural networks (C...