Bridging the Gap: A Framework for Real-World Video Deepfake Detection via Social Network Compression Emulation

TL;DR

This paper introduces a framework that emulates social network video compression to improve deepfake detection in real-world scenarios, addressing the challenge of generalization from controlled training data.

Contribution

It presents a novel method to replicate platform-specific video compression artifacts without API access, enhancing detector robustness in real-world social media environments.

Findings

Emulated data closely matches real social network video degradation patterns.

Detectors trained on emulated videos perform comparably to those trained on actual shared videos.

The approach improves deepfake detection robustness in real-world social media contexts.

Abstract

The growing presence of AI-generated videos on social networks poses new challenges for deepfake detection, as detectors trained under controlled conditions often fail to generalize to real-world scenarios. A key factor behind this gap is the aggressive, proprietary compression applied by platforms like YouTube and Facebook, which launder low-level forensic cues. However, replicating these transformations at scale is difficult due to API limitations and data-sharing constraints. For these reasons, we propose a first framework that emulates the video sharing pipelines of social networks by estimating compression and resizing parameters from a small set of uploaded videos. These parameters enable a local emulator capable of reproducing platform-specific artifacts on large datasets without direct API access. Experiments on FaceForensics++ videos shared via social networks demonstrate that our emulated data closely matches the degradation patterns of real uploads. Furthermore, detectors fine-tuned on emulated videos achieve comparable performance to those trained on actual shared media. Our approach offers a scalable and practical solution for bridging the gap between lab-based training and real-world deployment of deepfake detectors, particularly in the underexplored domain of compressed video content.