There have been two competing paradigms in artificial intelligence (AI) development ever
since its birth in 1956, ie, symbolism and connectionism (or sub-symbolism). While …

Deep Learning algorithms have achieved state-of-the-art performance for Image
Classification. For this reason, they have been used even in security-critical applications …

It has been recognized that the data generated by the denoising diffusion probabilistic
model (DDPM) improves adversarial training. After two years of rapid development in …

There is growing interest in ensuring that large language models (LLMs) align with human
values. However, the alignment of such models is vulnerable to adversarial jailbreaks, which …

As a research community, we are still lacking a systematic understanding of the progress on
adversarial robustness which often makes it hard to identify the most promising ideas in …

Deep neural networks (DNNs) are known to be vulnerable to adversarial examples. It is thus
imperative to devise effective attack algorithms to identify the deficiencies of DNNs …

Adversarial attacks to image classification systems present challenges to convolutional
networks and opportunities for understanding them. This study suggests that adversarial …

Deep learning models are vulnerable to adversarial examples crafted by applying human-
imperceptible perturbations on benign inputs. However, under the black-box setting, most …

Though CNNs have achieved the state-of-the-art performance on various vision tasks, they
are vulnerable to adversarial examples---crafted by adding human-imperceptible …

With rapid progress and significant successes in a wide spectrum of applications, deep
learning is being applied in many safety-critical environments. However, deep neural …