arXiv:2402.14825
공식 코드 · Papers with Code
Deepfake Detection and the Impact of Limited Computing Capabilities
원문 초록
The rapid development of technologies and artificial intelligence makes deepfakes an increasingly sophisticated and challenging-to-identify technique. To ensure the accuracy of information and control misinformation and mass manipulation, it is of paramount importance to discover and develop artificial intelligence models that enable the generic detection of forged videos. This work aims to address the detection of deepfakes across various existing datasets in a scenario with limited computing resources. The goal is to analyze the applicability of different deep learning techniques under these restrictions and explore possible approaches to enhance their efficiency.
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BusterX: MLLM-Powered AI-Generated Video Forgery Detection and Explanation
Rethinking Vision-Language Model in Face Forensics: Multi-Modal Interpretable Forged Face Detector
ExDDV: A New Dataset for Explainable Deepfake Detection in Video
SHIELD: A Secure and Highly Enhanced Integrated Learning for Robust Deepfake Detection against Adversarial Attacks
Lightweight Joint Audio-Visual Deepfake Detection via Single-Stream Multi-Modal Learning Framework
DeepFake Doctor: Diagnosing and Treating Audio-Video Fake Detection
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