Category Ranking
98%
Total Visits
921
Avg Visit Duration
2 minutes
Citations
20
Article Abstract
Multimedia data manipulation and forgery has never been easier than today, thanks to the power of Artificial Intelligence (AI). AI-generated fake content, commonly called Deepfakes, have been raising new issues and concerns, but also new challenges for the research community. The Deepfake detection task has become widely addressed, but unfortunately, approaches in the literature suffer from generalization issues. In this paper, the Face Deepfake Detection and Reconstruction Challenge is described. Two different tasks were proposed to the participants: (i) creating a Deepfake detector capable of working in an "in the wild" scenario; (ii) creating a method capable of reconstructing original images from Deepfakes. Real images from CelebA and FFHQ and Deepfake images created by StarGAN, StarGAN-v2, StyleGAN, StyleGAN2, AttGAN and GDWCT were collected for the competition. The winning teams were chosen with respect to the highest classification accuracy value (Task I) and "minimum average distance to Manhattan" (Task II). Deep Learning algorithms, particularly those based on the architecture, achieved the best results in Task I. No winners were proclaimed for Task II. A detailed discussion of teams' proposed methods with corresponding ranking is presented in this paper.
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9605671 | PMC |
| http://dx.doi.org/10.3390/jimaging8100263 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Hybrid Convolutional Neural Network and Generative Adversarial Network Framework for Robust Deepfake Detection: Advancing Accuracy, Scalability, and Multimodal Integration.
J Vis Exp
August 2025
Department of Computer Science and Engineering, Gokaraju Rangaraju Institute of Engineering and Technology, Hyderabad, India.
Deepfakes pose critical threats to digital media integrity and societal trust. This paper presents a hybrid deepfake detection framework combining Convolutional Neural Networks (CNNs) and Generative Adversarial Networks (GANs) to address challenges in scalability, generalizability, and adversarial robustness. The framework integrates adversarial training, a temporal decay analysis model, and multimodal detection across audio, video, and text domains.
View Article and Find Full Text PDFLearning Local Texture and Global Frequency Clues for Face Forgery Detection.
Biomimetics (Basel)
July 2025
Engineering Research Center of Cyberspace, Yunnan University, Kunming 650504, China.
In recent years, the rapid advancement of deep learning techniques has significantly propelled the development of face forgery methods, drawing considerable attention to face forgery detection. However, existing detection methods still struggle with generalization across different datasets and forgery techniques. In this work, we address this challenge by leveraging both local texture cues and global frequency domain information in a complementary manner to enhance the robustness of face forgery detection.
View Article and Find Full Text PDFHSFF-Net: Hierarchical spectral-feature fusion network for deepfake detection and localization.
Neural Netw
August 2025
Department of Mathematics, Xi'an University of Technology, Xi'an, 710048, China. Electronic address:
The rapid development of deepfake techniques poses a serious threat to multimedia authenticity, driving increased attention to deepfake detection. However, most existing methods focus solely on classification while overlooking forgery localization, which is essential for understanding manipulation intent. To address this issue, we propose a novel Hierarchical Spectral-Feature Fusion Network (HSFF-Net) for deepfake detection and localization from spatial- and frequency-domain views.
View Article and Find Full Text PDFFaces Blind Your Eyes: Unveiling the Content-Irrelevant Synthetic Artifacts for Deepfake Detection.
IEEE Trans Image Process
August 2025
Data synthesis methods have shown promising results in general deepfake detection tasks. This is attributed to the inherent blending process in deepfake creation, which leaves behind distinct synthetic artifacts. However, the existence of content-irrelevant artifacts has not been explicitly explored in the deepfake synthesis.
View Article and Find Full Text PDFSentiment analysis for deepfake X posts using novel transfer learning based word embedding and hybrid LGR approach.
Sci Rep
August 2025
Department of Information and Communication Engineering, Yeungnam University, Gyeongsan, 38541, Republic of Korea.
With the growth of social media, people are sharing more content than ever, including X posts that reflect a variety of emotions and opinions. AI-generated synthetic text, known as deepfake text, is used to imitate human writing to disseminate misleading information and fake news. However, as deepfake technology continues to grow, it becomes harder to accurately understand people's opinions on deepfake posts.
View Article and Find Full Text PDF