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Found 13 papers, 7 papers with code
MoE-FFD: Mixture of Experts for Generalized and Parameter-Efficient Face Forgery Detection
Moreover, MoE-FFD leverages the expressivity of transformers and local priors of CNNs to simultaneously extract global and local forgery clues.
Pixel-Inconsistency Modeling for Image Manipulation Localization
Digital image forensics plays a crucial role in image authentication and manipulation localization.
Forgery-aware Adaptive Vision Transformer for Face Forgery Detection
To circumvent these issues, we propose a novel Forgery-aware Adaptive Vision Transformer (FA-ViT).
S-Adapter: Generalizing Vision Transformer for Face Anti-Spoofing with Statistical Tokens
Face Anti-Spoofing (FAS) aims to detect malicious attempts to invade a face recognition system by presenting spoofed faces.
Beyond the Prior Forgery Knowledge: Mining Critical Clues for General Face Forgery Detection
Face forgery detection is essential in combating malicious digital face attacks.
Enhancing General Face Forgery Detection via Vision Transformer with Low-Rank Adaptation
Nowadays, forgery faces pose pressing security concerns over fake news, fraud, impersonation, etc.
M3FAS: An Accurate and Robust MultiModal Mobile Face Anti-Spoofing System
Face presentation attacks (FPA), also known as face spoofing, have brought increasing concerns to the public through various malicious applications, such as financial fraud and privacy leakage.
Two-branch Multi-scale Deep Neural Network for Generalized Document Recapture Attack Detection
The image recapture attack is an effective image manipulation method to erase certain forensic traces, and when targeting on personal document images, it poses a great threat to the security of e-commerce and other web applications.
Digital and Physical Face Attacks: Reviewing and One Step Further
With the rapid progress over the past five years, face authentication has become the most pervasive biometric recognition method.
Rethinking Attention-Model Explainability through Faithfulness Violation Test
Attention mechanisms are dominating the explainability of deep models.
No-Reference Image Quality Assessment by Hallucinating Pristine Features
In this paper, we propose a no-reference (NR) image quality assessment (IQA) method via feature level pseudo-reference (PR) hallucination.
Detect and Locate: Exposing Face Manipulation by Semantic- and Noise-level Telltales
The technological advancements of deep learning have enabled sophisticated face manipulation schemes, raising severe trust issues and security concerns in modern society.
Faster super-resolution imaging with auto-correlation two-step deconvolution
Despite super-resolution fluorescence blinking microscopes break the diffraction limit, the intense phototoxic illumination and long-term image sequences thus far still pose to major challenges in visualizing live-organisms.
Optics
