Multimodal Forgery Detection Using Ensemble Learning
The recent rapid revolution in Artificial Intelligence (AI) technology has enabled the creation of hyper-realistic deepfakes, and detecting deepfake videos (also known as AIsynthesized videos) has become a critical task. The existing systems generally do not fully consider the unified processing of audio and video data, so there is still room for further improvement. In this paper, we focus on the multimodal forgery detection task and propose a deep forgery detection method based on audiovisual ensemble learning. The proposed method consists of four parts, namely a Video Network, an Audio Network, an Audiovisual Network, and a Voting Module. Given a video, the proposed multimodal and ensemble learning system can identify whether it is fake or real. Experimental results on a recently released multimodal FakeAVCeleb dataset show that the proposed method achieves 89% accuracy, significantly outperforming existing models.
Code (1)
Tasks
Ensemble LearningFace SwappingMultimodal Forgery DetectionSimilar Papers 제목 키워드 기반
Consolidating Diffusion-Generated Video Detection with Unified Multimodal Forgery Learning
The proliferation of videos generated by diffusion models has raised increasing concerns about information security, highlighting the urgent need for reliable detection of synthetic media. Existing methods primarily focu…
ForgerySleuth: Empowering Multimodal Large Language Models for Image Manipulation Detection
Multimodal large language models have unlocked new possibilities for various multimodal tasks. However, their potential in image manipulation detection remains unexplored. When directly applied to the IMD task, M-LLMs of…
Image ManipulationImage Manipulation DetectionForgeryGPT: Multimodal Large Language Model For Explainable Image Forgery Detection and Localization
Multimodal Large Language Models (MLLMs), such as GPT4o, have shown strong capabilities in visual reasoning and explanation generation. However, despite these strengths, they face significant challenges in the increasing…
Explanation GenerationImage Forgery DetectionInstruction FollowingLanguage Modeling+4Towards General Visual-Linguistic Face Forgery Detection(V2)
Face manipulation techniques have achieved significant advances, presenting serious challenges to security and social trust. Recent works demonstrate that leveraging multimodal models can enhance the generalization and i…
HallucinationLanguage ModelingLanguage ModellingLarge Language Model+1DeepfakeBench-MM: A Comprehensive Benchmark for Multimodal Deepfake Detection
The misuse of advanced generative AI models has resulted in the widespread proliferation of falsified data, particularly forged human-centric audiovisual content, which poses substantial societal risks (e.g., financial f…
DeepFake Detection