DF-RAP: A Robust Adversarial Perturbation for Defending Against Deepfakes in Real-World Social Network Scenarios
Sign Up to like & getrecommendations! Published in 2024 at "IEEE Transactions on Information Forensics and Security"
DOI: 10.1109/tifs.2024.3372803
Abstract: The misuse of Deepfakes to create unauthorized fake facial images and videos poses a growing threat to personal privacy and social stability. Proactive defense algorithms have been proposed to prevent this fraud by injecting adversarial… read more here.
Keywords: robust adversarial; adversarial perturbation; rap; facial images ... See more keywords
Cost-effective and robust adversarial patch attacks in real-world scenarios
Sign Up to like & getrecommendations! Published in 2025 at "Journal of Electronic Imaging"
DOI: 10.1117/1.jei.34.3.033003
Abstract: Abstract. Deep neural networks (DNNs) are crucial in self-driving car technology but are vulnerable to adversarial attacks that manipulate visual input to mislead decision-making processes. Traditional physical adversarial attacks, such as patch-based methods, often use… read more here.
Keywords: patch; robust adversarial; effective robust; patch attacks ... See more keywords
URAdv: A Novel Framework for Generating Ultra-Robust Adversarial Patches Against UAV Object Detection
Sign Up to like & getrecommendations! Published in 2025 at "Mathematics"
DOI: 10.3390/math13040591
Abstract: In recent years, deep learning has been extensively deployed on unmanned aerial vehicles (UAVs), particularly for object detection. As the cornerstone of UAV-based object detection, deep neural networks are susceptible to adversarial attacks, with adversarial… read more here.
Keywords: robust adversarial; object detection; framework; adversarial patches ... See more keywords