Adversarial attacks and defenses in machine learning and deep neural network (DNN) have
been gaining significant attention due to the rapidly growing applications of deep learning in …

Deep learning (DL) has been widely used in various fields. However, its black-box nature
limits people's understanding and trust in its decision-making process. Therefore, it becomes …

The binary neural network, largely saving the storage and computation, serves as a
promising technique for deploying deep models on resource-limited devices. However, the …

Deep neural networks (DNNs) have become popular for medical image analysis tasks like
cancer diagnosis and lesion detection. However, a recent study demonstrates that medical …

Adversarial attacks are valuable for evaluating the robustness of deep learning models.
Existing attacks are primarily conducted on the visible light spectrum (eg, pixel-wise texture …

Deep learning models are vulnerable to adversarial examples. As a more threatening type
for practical deep learning systems, physical adversarial examples have received extensive …

Deep neural networks are exposed to the risk of adversarial attacks via the fast gradient sign
method (FGSM), projected gradient descent (PGD) attacks, and other attack algorithms …

Face swap** has drawn a lot of attention for its compelling performance. However, current
deepfake methods suffer the effects of obscure workflow and poor performance. To solve …

Adversarial examples are inputs with imperceptible perturbations that easily misleading
deep neural networks (DNNs). Recently, adversarial patch, with noise confined to a small …

Physical world adversarial attack is a highly practical and threatening attack, which fools real
world deep learning systems by generating conspicuous and maliciously crafted real world …