Abstract
During the past decade, image forensics has made rapid progress due to the growing concern of image content authenticity. In order to remove or conceal the traces that forensics based on, some farsighted forgers take advantage of so-called anti-forensics to make their forgery more convincing. To rebuild the credibility of forensics, many countermeasures against anti-forensics have been proposed. This paper presents a multi-purpose approach to detect various anti-forensics based on the architecture of Convolutional Neural Networks (CNN), which can automatically extract features and identify the forged types. Our model can detect various image anti-forensics both in binary and multi-class decision effectively. Experimental results show that the proposed method performs well for multiple well-known image anti-forensic methods.
X. Kang—This work was supported by NSFC (Grant nos. 61379155, U1536204, 61502547, 61332012, 61272453 and NSF of Guangdong province (Grant no. s2013020012788).
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Yu, J., Zhan, Y., Yang, J., Kang, X. (2017). A Multi-purpose Image Counter-anti-forensic Method Using Convolutional Neural Networks. In: Shi, Y., Kim, H., Perez-Gonzalez, F., Liu, F. (eds) Digital Forensics and Watermarking. IWDW 2016. Lecture Notes in Computer Science(), vol 10082. Springer, Cham. https://doi.org/10.1007/978-3-319-53465-7_1
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