22 citations · 45 across the 11 of their papers we have counts for
12 papers
Machine Pareidolia: Protecting Facial Image with Emotional Editing
Binh M. Le, Simon S. Woo
The proliferation of facial recognition (FR) systems has raised privacy concerns in the digital realm, as malicious uses of FR models pose a significant threat. Traditional counter…
QID: Efficient Query-Informed ViTs in Data-Scarce Regimes for OCR-free Visual Document Understanding
Binh M. Le, Shaoyuan Xu, Jinmiao Fu +6
In Visual Document Understanding (VDU) tasks, fine-tuning a pre-trained Vision-Language Model (VLM) with new datasets often falls short in optimizing the vision encoder to identify…
Gradient Alignment for Cross-Domain Face Anti-Spoofing
Binh M. Le, Simon S. Woo
Recent advancements in domain generalization (DG) for face anti-spoofing (FAS) have garnered considerable attention. Traditional methods have focused on designing learning objectiv…
SoK: Systematization and Benchmarking of Deepfake Detectors in a Unified Framework
Binh M. Le, Jiwon Kim, Simon S. Woo +3
Deepfakes have rapidly emerged as a serious threat to society due to their ease of creation and dissemination, triggering the accelerated development of detection technologies. How…
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…
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…
Why Do Facial Deepfake Detectors Fail?
Binh Le, Shahroz Tariq, Alsharif Abuadbba +2
Recent rapid advancements in deepfake technology have allowed the creation of highly realistic fake media, such as video, image, and audio. These materials pose significant challen…
Towards an Awareness of Time Series Anomaly Detection Models' Adversarial Vulnerability
Shahroz Tariq, Binh M. Le, Simon S. Woo
Time series anomaly detection is extensively studied in statistics, economics, and computer science. Over the years, numerous methods have been proposed for time series anomaly det…
KappaFace: Adaptive Additive Angular Margin Loss for Deep Face Recognition
Chingis Oinar, Binh M. Le, Simon S. Woo
Feature learning is a widely used method employed for large-scale face recognition. Recently, large-margin softmax loss methods have demonstrated significant enhancements on deep f…
Exploring the Asynchronous of the Frequency Spectra of GAN-generated Facial Images
Binh M. Le, Simon S. Woo
The rapid progression of Generative Adversarial Networks (GANs) has raised a concern of their misuse for malicious purposes, especially in creating fake face images. Although many…
ADD: Frequency Attention and Multi-View based Knowledge Distillation to Detect Low-Quality Compressed Deepfake Images
Binh M. Le, Simon S. Woo
Despite significant advancements of deep learning-based forgery detectors for distinguishing manipulated deepfake images, most detection approaches suffer from moderate to signific…