# Attentive Semantic Exploring for Manipulated Face Detection

arXiv: https://arxiv.org/abs/2005.02958 · HTML: https://mlsanity.com/paper/2005.02958/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 May 2020.

## Built on by (most cited first, 14 of 14)

- [Countering Malicious DeepFakes: Survey, Battleground, and Horizon](https://mlsanity.com/paper/2103.00218/) (2021)
- [Learning Pairwise Interaction for Generalizable DeepFake Detection](https://mlsanity.com/paper/2302.13288/) (2023)
- [Dual Frequency Branch Framework with Reconstructed Sliding Windows Attention for AI-Generated Image Detection](https://mlsanity.com/paper/2501.15253/) (2025)
- [Multimodal Graph Learning for Deepfake Detection](https://mlsanity.com/paper/2209.05419/) (2022)
- [DGS-Net: Distillation-Guided Gradient Surgery for CLIP Fine-Tuning in AI-Generated Image Detection](https://mlsanity.com/paper/2511.13108/) (2025)
- [Can We Find Neurons that Cause Unrealistic Images in Deep Generative Networks?](https://mlsanity.com/paper/2201.06346/) (2022)
- [Decoupling Forgery Semantics for Generalizable Deepfake Detection](https://mlsanity.com/paper/2406.09739/) (2024)
- [Automated Deepfake Detection](https://mlsanity.com/paper/2106.10705/) (2021)
- [Fighting Malicious Media Data: A Survey on Tampering Detection and Deepfake Detection](https://mlsanity.com/paper/2212.05667/) (2022)
- [Spritz-PS: Validation of Synthetic Face Images Using a Large Dataset of Printed Documents](https://mlsanity.com/paper/2304.02982/) (2023)
- [Datasets, Clues and State-of-the-Arts for Multimedia Forensics: An Extensive Review](https://mlsanity.com/paper/2401.06999/) (2024)
- [Federated Face Forgery Detection Learning with Personalized Representation](https://mlsanity.com/paper/2406.11145/) (2024)
- [Deep Learning Technology for Face Forgery Detection: A Survey](https://mlsanity.com/paper/2409.14289/) (2024)
- [Beyond Natural Images: Rethinking AI-Generated Image Detection in Documents](https://mlsanity.com/paper/2609.14352/) (2026)

## Abstract (arXiv)

Face manipulation methods develop rapidly in recent years, whose potential risk to society accounts for the emerging of researches on detection methods. However, due to the diversity of manipulation methods and the high quality of fake images, detection methods suffer from a lack of generalization ability. To solve the problem, we find that segmenting images into semantic fragments could be effective, as discriminative defects and distortions are closely related to such fragments. Besides, to highlight discriminative regions in fragments and to measure contribution to the final prediction of each fragment is efficient for the improvement of generalization ability. Therefore, we propose a novel manipulated face detection method based on Multilevel Facial Semantic Segmentation and Cascade Attention Mechanism. To evaluate our method, we reconstruct two datasets: GGFI and FFMI, and also collect two open-source datasets. Experiments on four datasets verify the advantages of our approach against other state-of-the-arts, especially its generalization ability.