Conference proceeding
Mislgan: An Anti-Forensic Camera Model Falsification Framework Using A Generative Adversarial Network
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) Conference Proceedings, 535
01 Jan 2018
Abstract
Conference Title: 2018 25th IEEE International Conference on Image Processing (ICIP) Conference Start Date: 2018, Oct. 7 Conference End Date: 2018, Oct. 10 Conference Location: Athens, Greece Deep learning techniques have become popular for performing camera model identification. To expose weaknesses in these methods, we propose a new anti-forensic framework that utilizes a generative adversarial network (GAN) to falsify an image's source camera model. Our proposed attack uses the generator trained in the GAN to produce an image that can fool a CNN-based camera model identification classifier. Moreover, our proposed attack will only introduce a minimal amount of distortion to the falsified image that is not perceptible to human eyes. By conducting experiments on a large amount of data, we show that the proposed attack can successfully fool a state-of-art camera model identification CNN classifier with 98% probability and maintain high image quality.
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Details
- Title
- Mislgan: An Anti-Forensic Camera Model Falsification Framework Using A Generative Adversarial Network
- Creators
- Chen ChenXinwei ZhaoMatthew C Stamm
- Publication Details
- The Institute of Electrical and Electronics Engineers, Inc. (IEEE) Conference Proceedings, 535
- Publisher
- The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Electrical and Computer Engineering
- Identifiers
- 991019170495504721