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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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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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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