DeepFake-O-Meter v2.0: An Open Platform for DeepFake Detection
- URL: http://arxiv.org/abs/2404.13146v2
- Date: Thu, 27 Jun 2024 07:02:48 GMT
- Title: DeepFake-O-Meter v2.0: An Open Platform for DeepFake Detection
- Authors: Yan Ju, Chengzhe Sun, Shan Jia, Shuwei Hou, Zhaofeng Si, Soumyya Kanti Datta, Lipeng Ke, Riky Zhou, Anita Nikolich, Siwei Lyu,
- Abstract summary: DeepFake-O-Meter integrates state-of-the-art methods for detecting Deepfake images, videos, and audio.
The platform aims to offer everyday users a convenient service for analyzing DeepFake media using multiple state-of-the-art detection algorithms.
- Score: 23.879850908167278
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Deepfakes, as AI-generated media, have increasingly threatened media integrity and personal privacy with realistic yet fake digital content. In this work, we introduce an open-source and user-friendly online platform, DeepFake-O-Meter v2.0, that integrates state-of-the-art methods for detecting Deepfake images, videos, and audio. Built upon DeepFake-O-Meter v1.0, we have made significant upgrades and improvements in platform architecture design, including user interaction, detector integration, job balancing, and security management. The platform aims to offer everyday users a convenient service for analyzing DeepFake media using multiple state-of-the-art detection algorithms. It ensures secure and private delivery of the analysis results. Furthermore, it serves as an evaluation and benchmarking platform for researchers in digital media forensics to compare the performance of multiple algorithms on the same input. We have also conducted detailed usage analysis based on the collected data to gain deeper insights into our platform's statistics. This involves analyzing two-month trends in user activity and evaluating the processing efficiency of each detector.
Related papers
- A Multimodal Framework for Deepfake Detection [0.0]
Deepfakes, synthetic media created using AI, can convincingly alter videos and audio to misrepresent reality.
Our research addresses the critical issue of deepfakes through an innovative multimodal approach.
Our framework combines visual and auditory analyses, yielding an accuracy of 94%.
arXiv Detail & Related papers (2024-10-04T14:59:10Z) - Leveraging Mixture of Experts for Improved Speech Deepfake Detection [53.69740463004446]
Speech deepfakes pose a significant threat to personal security and content authenticity.
We introduce a novel approach for enhancing speech deepfake detection performance using a Mixture of Experts architecture.
arXiv Detail & Related papers (2024-09-24T13:24:03Z) - Semantics-Oriented Multitask Learning for DeepFake Detection: A Joint Embedding Approach [77.65459419417533]
We propose an automatic dataset expansion technique to support semantics-oriented DeepFake detection tasks.
We also resort to joint embedding of face images and their corresponding labels for prediction.
Our method improves the generalizability of DeepFake detection and renders some degree of model interpretation by providing human-understandable explanations.
arXiv Detail & Related papers (2024-08-29T07:11:50Z) - Integrating Audio-Visual Features for Multimodal Deepfake Detection [33.51027054306748]
Deepfakes are AI-generated media in which an image or video has been digitally modified.
This paper proposes an audio-visual-based method for deepfake detection, which integrates fine-grained deepfake identification with binary classification.
arXiv Detail & Related papers (2023-10-05T18:19:56Z) - CrossDF: Improving Cross-Domain Deepfake Detection with Deep Information Decomposition [53.860796916196634]
We propose a Deep Information Decomposition (DID) framework to enhance the performance of Cross-dataset Deepfake Detection (CrossDF)
Unlike most existing deepfake detection methods, our framework prioritizes high-level semantic features over specific visual artifacts.
It adaptively decomposes facial features into deepfake-related and irrelevant information, only using the intrinsic deepfake-related information for real/fake discrimination.
arXiv Detail & Related papers (2023-09-30T12:30:25Z) - DeepfakeBench: A Comprehensive Benchmark of Deepfake Detection [55.70982767084996]
A critical yet frequently overlooked challenge in the field of deepfake detection is the lack of a standardized, unified, comprehensive benchmark.
We present the first comprehensive benchmark for deepfake detection, called DeepfakeBench, which offers three key contributions.
DeepfakeBench contains 15 state-of-the-art detection methods, 9CL datasets, a series of deepfake detection evaluation protocols and analysis tools, as well as comprehensive evaluations.
arXiv Detail & Related papers (2023-07-04T01:34:41Z) - Finding Facial Forgery Artifacts with Parts-Based Detectors [73.08584805913813]
We design a series of forgery detection systems that each focus on one individual part of the face.
We use these detectors to perform detailed empirical analysis on the FaceForensics++, Celeb-DF, and Facebook Deepfake Detection Challenge datasets.
arXiv Detail & Related papers (2021-09-21T16:18:45Z) - DeepFake-o-meter: An Open Platform for DeepFake Detection [36.62547135445819]
We develop an open-source online platform, known as DeepFake-o-meter, that integrates state-of-the-art DeepFake detection methods.
We describe the design and function of DeepFake-o-meter in this work.
arXiv Detail & Related papers (2021-03-02T20:45:33Z) - Training Strategies and Data Augmentations in CNN-based DeepFake Video
Detection [17.696134665850447]
The accuracy of automated systems for face forgery detection in videos is still quite limited and generally biased toward the dataset used to design and train a specific detection system.
In this paper we analyze how different training strategies and data augmentation techniques affect CNN-based deepfake detectors when training and testing on the same dataset or across different datasets.
arXiv Detail & Related papers (2020-11-16T08:50:56Z) - Emotions Don't Lie: An Audio-Visual Deepfake Detection Method Using
Affective Cues [75.1731999380562]
We present a learning-based method for detecting real and fake deepfake multimedia content.
We extract and analyze the similarity between the two audio and visual modalities from within the same video.
We compare our approach with several SOTA deepfake detection methods and report per-video AUC of 84.4% on the DFDC and 96.6% on the DF-TIMIT datasets.
arXiv Detail & Related papers (2020-03-14T22:07:26Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.