Search Results for author:
Found 8 papers, 3 papers with code
AVT2-DWF: Improving Deepfake Detection with Audio-Visual Fusion and Dynamic Weighting Strategies
With the continuous improvements of deepfake methods, forgery messages have transitioned from single-modality to multi-modal fusion, posing new challenges for existing forgery detection algorithms.
Dual Defense: Adversarial, Traceable, and Invisible Robust Watermarking against Face Swapping
Dual Defense invisibly embeds a single robust watermark within the target face to actively respond to sudden cases of malicious face swapping.
Universal Defensive Underpainting Patch: Making Your Text Invisible to Optical Character Recognition
In this work, we propose a novel and effective defense mechanism termed the Universal Defensive Underpainting Patch (UDUP) that modifies the underpainting of text images instead of the characters.
Optical Character Recognition
Optical Character Recognition (OCR)
Detecting Backdoors During the Inference Stage Based on Corruption Robustness Consistency
Deep neural networks are proven to be vulnerable to backdoor attacks.
Feature Extraction Matters More: Universal Deepfake Disruption through Attacking Ensemble Feature Extractors
Adversarial example is a rising way of protecting facial privacy security from deepfake modification.
Implicit Identity Driven Deepfake Face Swapping Detection
Face swapping aims to replace the target face with the source face and generate the fake face that the human cannot distinguish between real and fake.
Detection Defense Against Adversarial Attacks with Saliency Map
Our experimental results of some representative adversarial attacks on common datasets including ImageNet and popular models show that our method can detect all the attacks with high detection success rate effectively.
NEXT: A Neural Network Framework for Next POI Recommendation
Inspired by the recent success of neural networks in many areas, in this paper, we present a simple but effective neural network framework for next POI recommendation, named NEXT.
