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Found 13 papers, 2 papers with code
A Quality-Centric Framework for Generic Deepfake Detection
To tackle this issue, we propose a novel quality-centric framework for generic deepfake detection, which is composed of a Quality Evaluator, a low-quality data enhancement module, and a learning pacing strategy that explicitly incorporates forgery quality into the training process.
Image Copy-Move Forgery Detection via Deep PatchMatch and Pairwise Ranking Learning
To address these limitations, this study proposes a novel end-to-end CMFD framework that integrates the strengths of conventional and deep learning methods.
Multi-modal Document Presentation Attack Detection With Forensics Trace Disentanglement
Document Presentation Attack Detection (DPAD) is an important measure in protecting the authenticity of a document image.
SHIELD : An Evaluation Benchmark for Face Spoofing and Forgery Detection with Multimodal Large Language Models
For the face forgery detection task, we evaluate GAN-based and diffusion-based data with both visual and acoustic modalities.
CMA: A Chromaticity Map Adapter for Robust Detection of Screen-Recapture Document Images
Then we extract a chromaticity map from the recaptured image to highlight the presence of color artifacts even under low-quality samples.
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.
Image Copy-Move Forgery Detection via Deep Cross-Scale PatchMatch
The recently developed deep algorithms achieve promising progress in the field of image copy-move forgery detection (CMFD).
Visual Prompt Flexible-Modal Face Anti-Spoofing
Recently, vision transformer based multimodal learning methods have been proposed to improve the robustness of face anti-spoofing (FAS) systems.
Forensicability Assessment of Questioned Images in Recapturing Detection
We integrate the trained FANet with practical recapturing detection schemes in face anti-spoofing and recaptured document detection tasks.
DRL-FAS: A Novel Framework Based on Deep Reinforcement Learning for Face Anti-Spoofing
Inspired by the philosophy employed by human beings to determine whether a presented face example is genuine or not, i. e., to glance at the example globally first and then carefully observe the local regions to gain more discriminative information, for the face anti-spoofing problem, we propose a novel framework based on the Convolutional Neural Network (CNN) and the Recurrent Neural Network (RNN).
Detection of Information Hiding at Anti-Copying 2D Barcodes
Our experimental results show that the PVBD scheme can correctly detect the existence of the hidden information at both the 2LQR code and the LCAC 2D barcode.
Layer Pruning for Accelerating Very Deep Neural Networks
In this paper, we propose an adaptive pruning method.
Learning deep forest with multi-scale Local Binary Pattern features for face anti-spoofing
Studies about the transferability of the adversarial attack reveal that utilizing handcrafted feature-based methods could improve security in a system-level.
