Abstract
Discriminating between computer-generated images (CGIs) and photographic images (PIs) is not a new problem in digital image forensics. However, with advances in rendering techniques supported by strong hardware and in generative adversarial networks, CGIs are becoming indistinguishable from PIs in both human and computer perception. This means that malicious actors can use CGIs for spoofing facial authentication systems, impersonating other people, and creating
fake news to be spread on social networks. The methods developed for discriminating between CGIs and PIs quickly become outdated and must be regularly enhanced to be able to reduce these attack surfaces. Leveraging recent advances in deep convolutional networks, we have built a modular CGI–PI discriminator with a customized VGG-19 network as the feature extractor, statistical convolutional neural networks as the feature transformers, and a discriminator. We also devised a probabilistic patch aggregation strategy to deal with high-resolution images. This proposed method outperformed a state-of-the-art method and achieved accuracy up to 100%.
fake news to be spread on social networks. The methods developed for discriminating between CGIs and PIs quickly become outdated and must be regularly enhanced to be able to reduce these attack surfaces. Leveraging recent advances in deep convolutional networks, we have built a modular CGI–PI discriminator with a customized VGG-19 network as the feature extractor, statistical convolutional neural networks as the feature transformers, and a discriminator. We also devised a probabilistic patch aggregation strategy to deal with high-resolution images. This proposed method outperformed a state-of-the-art method and achieved accuracy up to 100%.
Original language | English |
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Title of host publication | 13th International Conference on Availability, Reliability and Security (ARES 2018) |
Place of Publication | Hamburg, Germany |
Publisher | ACM |
Pages | 1:1-1:10 |
Number of pages | 10 |
ISBN (Print) | 978-1-4503-6448-5 |
DOIs | |
Publication status | Published - 27 Aug 2018 |
Event | 13th International Conference on Availability, Reliability and Security - Hamburg, Germany Duration: 27 Aug 2018 → 30 Aug 2018 https://www.ares-conference.eu/conference-2018/cfp2018/ |
Conference
Conference | 13th International Conference on Availability, Reliability and Security |
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Abbreviated title | ARES 2018 |
Country/Territory | Germany |
City | Hamburg |
Period | 27/08/18 → 30/08/18 |
Internet address |