Learning JPEG Compression Artifacts for Image Manipulation Detection and Localization

Cited 0 time in webofscience Cited 0 time in scopus
  • Hit : 62
  • Download : 0
Detecting and localizing image manipulation are necessary to counter malicious use of image editing techniques. Accordingly, it is essential to distinguish between authentic and tampered regions by analyzing intrinsic statistics in an image. We focus on JPEG compression artifacts left during image acquisition and editing. We propose a convolutional neural network that uses discrete cosine transform (DCT) coefficients, where compression artifacts remain, to localize image manipulation. Standard CNNs cannot learn the distribution of DCT coefficients because the convolution throws away the spatial coordinates, which are essential for DCT coefficients. We illustrate how to design and train a neural network that can learn the distribution of DCT coefficients. Furthermore, we introduce Compression Artifact Tracing Network that jointly uses image acquisition artifacts and compression artifacts. It significantly outperforms traditional and deep neural network-based methods in detecting and localizing tampered regions.
Publisher
SPRINGER
Issue Date
2022-08
Language
English
Article Type
Article
Citation

INTERNATIONAL JOURNAL OF COMPUTER VISION, v.130, no.8, pp.1875 - 1895

ISSN
0920-5691
DOI
10.1007/s11263-022-01617-5
URI
http://hdl.handle.net/10203/297411
Appears in Collection
CS-Journal Papers(저널논문)EE-Journal Papers(저널논문)
Files in This Item
There are no files associated with this item.

qr_code

  • mendeley

    citeulike


rss_1.0 rss_2.0 atom_1.0