Detecting composite image manipulation based on deep neural networks

In this paper, we propose a composite manipulation detection method based on convolutional neural networks (CNNs). To our best knowledge, this is the first work applying deep learning for composite forgery detection. The proposed technique defines three types of attacks that occurred frequently during image forging and detects when they are concurrently applied to images. To do this, we learn the statistical change due to the manipulation through the proposed CNN architecture and classify the manipulated image. The proposed technique is effective since it learns integrated image of composite manipulation and extracts characteristic distinguished from original image. Since most attacks are applied in a composite way in real environment, the approach of the proposed technique has practical advantages compared to traditional forensics scheme. In addition, the experimental results demonstrate the reliability of the proposed method through results of high performance.
Publisher
IEEE Computer Society
Issue Date
2017-05-22
Language
English
Citation

24th International Conference on Systems, Signals and Image Processing, IWSSIP 2017, pp.1 - 5

DOI
10.1109/IWSSIP.2017.7965621
URI
http://hdl.handle.net/10203/225712
Appears in Collection
CS-Conference Papers(학술회의논문)
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