Over the past decade, deep learning has revolutionized conventional tasks that rely on hand-
craft feature extraction with its strong feature learning capability, leading to substantial …

Visual language pre-training (VLP) models have demonstrated significant success in
various domains, but they remain vulnerable to adversarial attacks. Addressing these …

With the emergence of foundation models, deep learning-based object detectors have
shown practical usability in closed set scenarios. However, for real-world tasks, object …

Object detection models, which are widely used in various domains (such as retail), have
been shown to be vulnerable to adversarial attacks. Existing methods for detecting …

Deep neural networks (DNNs) have revolutionized the field of computer vision like object
detection with their unparalleled performance. However, existing research has shown that …

Object detection techniques for Unmanned Aerial Vehicles (UAVs) rely on Deep Neural
Networks (DNNs), which are vulnerable to adversarial attacks. Nonetheless, adversarial …

Physical adversarial attacks in object detection have become an attractive topic. Many works
have proposed adversarial patches or camouflage to perform successful attacks in the real …

Recent years have seen an increasing interest in physical adversarial attacks, which aim to
craft deployable patterns for deceiving deep neural networks, especially for person …