Deep Learning is the most widely used tool in the contemporary field of computer vision. Its
ability to accurately solve complex problems is employed in vision research to learn deep …

GAN inversion aims to invert a given image back into the latent space of a pretrained GAN
model so that the image can be faithfully reconstructed from the inverted code by the …

Deep learning is at the heart of the current rise of artificial intelligence. In the field of
computer vision, it has become the workhorse for applications ranging from self-driving cars …

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 (AT) is always formulated as a minimax problem, of which the
performance depends on the inner optimization that involves the generation of adversarial …

Recent studies have demonstrated that existing deep neural networks (DNNs) on 3D point
clouds are vulnerable to adversarial examples, especially under the white-box settings …

Deep neural networks (DNNs) have found widespread applications in interpreting remote
sensing (RS) imagery. However, it has been demonstrated in previous works that DNNs are …

Backdoor attack intends to inject hidden backdoor into the deep neural networks (DNNs),
such that the prediction of the infected model will be maliciously changed if the hidden …

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

Deep neural networks (DNNs) have been shown to be vulnerable to adversarial examples,
which can produce erroneous predictions by injecting imperceptible perturbations. In this …