96 citations · 355 across the 27 of their papers we have counts for
28 papers
Quality-Agnostic Deepfake Detection with Intra-model Collaborative Learning
Binh M. Le, Simon S. Woo
Deepfake has recently raised a plethora of societal concerns over its possible security threats and dissemination of fake information. Much research on deepfake detection has been…
Towards Understanding of Deepfake Videos in the Wild
Beomsang Cho, Binh M. Le, Jiwon Kim +4
Deepfakes have become a growing concern in recent years, prompting researchers to develop benchmark datasets and detection algorithms to tackle the issue. However, existing dataset…
Unveiling Vulnerabilities in Interpretable Deep Learning Systems with Query-Efficient Black-box Attacks
Eldor Abdukhamidov, Mohammed Abuhamad, Simon S. Woo +2
Deep learning has been rapidly employed in many applications revolutionizing many industries, but it is known to be vulnerable to adversarial attacks. Such attacks pose a serious t…
HRFNet: High-Resolution Forgery Network for Localizing Satellite Image Manipulation
Fahim Faisal Niloy, Kishor Kumar Bhaumik, Simon S. Woo
Existing high-resolution satellite image forgery localization methods rely on patch-based or downsampling-based training. Both of these training methods have major drawbacks, such…
Microbial Genetic Algorithm-based Black-box Attack against Interpretable Deep Learning Systems
Eldor Abdukhamidov, Mohammed Abuhamad, Simon S. Woo +2
Deep learning models are susceptible to adversarial samples in white and black-box environments. Although previous studies have shown high attack success rates, coupling DNN models…
Bridging Optimal Transport and Jacobian Regularization by Optimal Trajectory for Enhanced Adversarial Defense
Binh M. Le, Shahroz Tariq, Simon S. Woo
Deep neural networks, particularly in vision tasks, are notably susceptible to adversarial perturbations. To overcome this challenge, developing a robust classifier is crucial. In…