# Securing Social Media Against Deepfakes using Identity, Behavioral, and Geometric Signatures

arXiv: https://arxiv.org/abs/2412.05487 · HTML: https://mlsanity.com/paper/2412.05487/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 66 references that are in the map.
- First posted on arXiv 7 Dec 2024.

## Builds on (most cited first, 5 of 5)

- [Exposing DeepFake Videos By Detecting Face Warping Artifacts](https://mlsanity.com/paper/1811.00656/) (2018)
- [The DeepFake Detection Challenge (DFDC) Dataset](https://mlsanity.com/paper/2006.07397/) (2020)
- [In Ictu Oculi: Exposing AI Generated Fake Face Videos by Detecting Eye Blinking](https://mlsanity.com/paper/1806.02877/) (2018)
- [GM-DF: Generalized Multi-Scenario Deepfake Detection](https://mlsanity.com/paper/2406.20078/) (2024)
- [Avatar Fingerprinting for Authorized Use of Synthetic Talking-Head Videos](https://mlsanity.com/paper/2305.03713/) (2023)

## Built on by (most cited first, 1 of 1)

- [SHIELD: A Secure and Highly Enhanced Integrated Learning for Robust Deepfake Detection against Adversarial Attacks](https://mlsanity.com/paper/2507.13170/) (2025)

## Abstract (arXiv)

Trust in social media is a growing concern due to its ability to influence significant societal changes. However, this space is increasingly compromised by various types of deepfake multimedia, which undermine the authenticity of shared content. Although substantial efforts have been made to address the challenge of deepfake content, existing detection techniques face a major limitation in generalization: they tend to perform well only on specific types of deepfakes they were trained on.This dependency on recognizing specific deepfake artifacts makes current methods vulnerable when applied to unseen or varied deepfakes, thereby compromising their performance in real-world applications such as social media platforms. To address the generalizability of deepfake detection, there is a need for a holistic approach that can capture a broader range of facial attributes and manipulations beyond isolated artifacts. To address this, we propose a novel deepfake detection framework featuring an effective feature descriptor that integrates Deep identity, Behavioral, and Geometric (DBaG) signatures, along with a classifier named DBaGNet. Specifically, the DBaGNet classifier utilizes the extracted DBaG signatures, leveraging a triplet loss objective to enhance generalized representation learning for improved classification. Specifically, the DBaGNet classifier utilizes the extracted DBaG signatures and applies a triplet loss objective to enhance generalized representation learning for improved classification. To test the effectiveness and generalizability of our proposed approach, we conduct extensive experiments using six benchmark deepfake datasets: WLDR, CelebDF, DFDC, FaceForensics++, DFD, and NVFAIR. Specifically, to ensure the effectiveness of our approach, we perform cross-dataset evaluations, and the results demonstrate significant performance gains over several state-of-the-art methods.