Detection of Deepfake Videos and Audios on Social Media Platforms

Charles Fross, QINGZHONG LIU

Keywords: deepfake, Deepfake Offensive Toolkit, Faceswap, DeepFaceLab, Voicemod, Real-Time Voice Cloning, metadata analysis, social media, deepfake detection, deep learning, digital forensics

TL;DR: This paper shows current deepfake detection models struggle with advanced tools like Haotian AI, and metadata changes from social media have minimal impact, stressing the need for more robust, multimodal detection methods.

Abstract: Deepfake technology, a rapidly evolving application of artificial intelligence, has enabled the creation of highly realistic yet synthetic multimedia content. While this innovation offers potential benefits in areas such as entertainment and education, its misuse has raised significant ethical and security concerns, including misinformation and financial fraud. This study evaluates the effectiveness of current deepfake detection methods, focusing on the Xception model for video detection and the LCNN model for audio detection, using a dataset composed of real-life and deepfake content. The dataset includes deepfakes generated by tools such as the Deepfake Offensive Toolkit and Haotian AI, a cutting-edge provider known for its high-quality outputs. Our findings reveal that the Xception model, while achieving 89.1% accuracy on control datasets, struggled to detect Haotian AI-generated deepfakes, misclassifying nearly all samples as authentic. This performance gap highlights the need for more diverse training datasets and advanced detection frameworks capable of addressing the nuances of emerging deepfake tools. Additionally, metadata changes caused by uploading and downloading content on social media platforms were found to have minimal impact on detection accuracy, challenging the feasibility of metadata-based detection approaches. This research underscores the limitations of current deepfake detection models and emphasizes the necessity for multimodal approaches and broader datasets to enhance robustness. The study’s implications call for continued advancements in detection methods to keep pace with the growing sophistication of deepfake technologies.

Submission Number: 3