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Found 8 papers, 6 papers with code
Gradient Alignment for Cross-Domain Face Anti-Spoofing
Recent advancements in domain generalization (DG) for face anti-spoofing (FAS) have garnered considerable attention.
SoK: Facial Deepfake Detectors
Our systematized analysis and experimentation lay the groundwork for a deeper understanding of deepfake detectors and their generalizability, paving the way for future research focused on creating detectors adept at countering various attack scenarios.
Quality-Agnostic Deepfake Detection with Intra-model Collaborative Learning
However, detecting low quality as well as simultaneously detecting different qualities of deepfakes still remains a grave challenge.
Bridging Optimal Transport and Jacobian Regularization by Optimal Trajectory for Enhanced Adversarial Defense
Our work is the first carefully analyzes and characterizes these two schools of approaches, both theoretically and empirically, to demonstrate how each approach impacts the robust learning of a classifier.
Towards an Awareness of Time Series Anomaly Detection Models' Adversarial Vulnerability
To the best of our understanding, we demonstrate, for the first time, the vulnerabilities of anomaly detection systems against adversarial attacks.
KappaFace: Adaptive Additive Angular Margin Loss for Deep Face Recognition
However, the majority of the proposed methods do not consider the class imbalance issue, which is a major challenge in practice for developing deep face recognition models.
Exploring the Asynchronous of the Frequency Spectra of GAN-generated Facial Images
The rapid progression of Generative Adversarial Networks (GANs) has raised a concern of their misuse for malicious purposes, especially in creating fake face images.
ADD: Frequency Attention and Multi-View based Knowledge Distillation to Detect Low-Quality Compressed Deepfake Images
In particular, we propose the Attention-based Deepfake detection Distiller (ADD), which consists of two novel distillations: 1) frequency attention distillation that effectively retrieves the removed high-frequency components in the student network, and 2) multi-view attention distillation that creates multiple attention vectors by slicing the teacher's and student's tensors under different views to transfer the teacher tensor's distribution to the student more efficiently.
