Backdoor attack has emerged as a major security threat to deep neural networks (DNNs).
While existing defense methods have demonstrated promising results on detecting or …

Backdoor attack intends to embed hidden backdoors into deep neural networks (DNNs), so
that the attacked models perform well on benign samples, whereas their predictions will be …

Adversarial training is one effective approach for training robust deep neural networks
against adversarial attacks. While being able to bring reliable robustness, adversarial …

Artificial Intelligence (AI) relies heavily on deep learning-a technology that is becoming
increasingly popular in real-life applications of AI, even in the safety-critical and high-risk …

Delusive attacks aim to substantially deteriorate the test accuracy of the learning model by
slightly perturbing the features of correctly labeled training examples. By formalizing this …

The backdoor or Trojan attack is a severe threat to deep neural networks (DNNs).
Researchers find that DNNs trained on benign data and settings can also learn backdoor …

Deep neural networks (DNNs) are vulnerable to backdoor attack, which does not affect the
network's performance on clean data but would manipulate the network behavior once a …

This paper proposes a simple method to distill and detect backdoor patterns within an
image:\emph {Cognitive Distillation}(CD). The idea is to extract the" minimal essence" from …

We present a novel defense, against backdoor attacks on Deep Neural Networks (DNNs),
wherein adversaries covertly implant malicious behaviors (backdoors) into DNNs. Our …

Despite the recent progress in deep neural networks (DNNs), it remains challenging to
explain the predictions made by DNNs. Existing explanation methods for DNNs mainly focus …