Existing adversarial attacks against Object Detectors (ODs) have two inherent limitations.
Firstly, ODs have complex meta-structure designs, hence most advanced attacks for ODs …

Although adversarial attacks have revealed weaknesses in Deep Neural Networks (DNNs)-
based aerial detectors, they present a new paradigm for concealing vulnerable assets from …

Targeted Attacks on Object Detection (TAOD) aim to deceive the victim detector into
recognizing a specific instance as the predefined target category while minimizing the …

In synthetic aperture radar (SAR) imaging, intelligent object detection methods are facing
significant challenges in terms of model robustness and application security, which are …

Deep neural networks (DNNs) are vulnerable to adversarial attacks, which can cause
security risks in computer information systems. Feature disruption attacks, as a typical form …

Adversarial attacks aim to perturb images such that a predictor outputs incorrect results. Due
to the limited research in structured attacks imposing consistency checks on natural multi …

In the field of object detection, the adversarial attack method based on generative
adversarial network efficiently generates adversarial examples, thereby significantly …

Object detection is widely employed in security-critical scenarios. With the rapid
development of deep learning, deep learning-based object detection methods have …

The existence of adversarial examples causes serious security risks when deep neural
networks are applied to synthetic aperture radar (SAR) target detection. In SAR image …

Deep learning models excel in various computer vision tasks but are susceptible to
adversarial examples-subtle perturbations in input data that lead to incorrect predictions …