The DOI matches its Crossref record; 34 of the 34 references it cites were found in a registry or an index.
Detection of GAN-Generated Fake Images over Social Networks
Abstract
The diffusion of fake images and videos on social networks is a fast growing problem. Commercial media editing tools allow anyone to remove, add, or clone people and objects, to generate fake images. Many techniques have been proposed to detect such conventional fakes, but new attacks emerge by the day. Image-to-image translation, based on generative adversarial networks (GANs), appears as one of the most dangerous, as it allows one to modify context and semantics of images in a very realistic way. In this paper, we study the performance of several image forgery detectors against image-to-image translation, both in ideal conditions, and in the presence of compression, routinely performed upon uploading on social networks. The study, carried out on a dataset of 36302 images, shows that detection accuracies up to 95% can be achieved by both conventional and deep learning detectors, but only the latter keep providing a high accuracy, up to 89%, on compressed data.
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Deep Residual Learning for Image Recognition
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Densely Connected Convolutional Networks
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Image-to-Image Translation with Conditional Adversarial Networks
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GAN（Generative Adversarial Nets）
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Unsupervised Image-to-Image Translation NetworksPreprint
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Sorted by citations
Face X-Ray for More General Face Forgery Detection
Li, L., Bao, J., Zhang, T., Yang, H., Chen, D., Wen, F., & Guo, B. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00505 · Cited by 1,211
CNN-Generated Images Are Surprisingly Easy to Spot… for Now
Wang, S.-Y., Wang, O., Zhang, R., Owens, A., & Efros, A. A. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00872 · Cited by 1,210
Media Forensics and DeepFakes: An Overview
Verdoliva, L. (2020) · IEEE Journal of Selected Topics in Signal Processing · doi:10.1109/jstsp.2020.3002101 · Cited by 979
Self-citationThinking in Frequency: Face Forgery Detection by Mining Frequency-Aware Clues
Qian, Y., Yin, G., Sheng, L., Chen, Z., & Shao, J. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-58610-2_6 · Cited by 1,058
The Creation and Detection of Deepfakes: A Survey
Mirsky, Y., & Lee, W. (2022) · ACM Computing Surveys · doi:10.1145/3425780 · Cited by 863
Metadata
- Authors
- Marra, F., Gragnaniello, D., Cozzolino, D., & Verdoliva, L.
- Affiliations
- University of Naples Federico II (IT)
- Venue
- 2018 IEEE Conference on Multimedia Information Processing and Retrieval (MIPR)
- Publisher
- IEEE
- Published
- 2018-04
- Pages
- 384-389
- OpenAlex
- W2811414481
- Cited by
- 399
- References
- 34
- Citations by year
- 2026: 19 · 2025: 44 · 2024: 68 · 2023: 59 · 2022: 65 · 2021: 58 · 2020: 58 · 2019: 21 · 2018: 7
- FWCI
- 10.85
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE International Conference on Multimedia Information Processing and Retrieval (MIPR) (publishedVersion)
- Funders
- Air Force Research Laboratory
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Digital Media Forensic Detection
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Steganography and Watermarking Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Generative Adversarial Networks and Image Synthesis
- Keywords
- image forgery detection, GAN-generated images, image-to-image translation, deep learning detector, image compression, deepfakes, media manipulation
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