Conference proceeding
On the robustness of constrained convolutional neural networks to JPEG post-compression for image resampling detection
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) Conference Proceedings, 2152
01 Jan 2017
Abstract
Conference Title: ICASSP 2017 - 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Conference Start Date: 2017, March 5 Conference End Date: 2017, March 9 Conference Location: New Orleans, LA, USA Detecting image resampling in re-compressed images is a very challenging problem. Existing approaches to image resampling detection operate by building pre-selected model to locate periodicities in linear predictor residues. Additionally, if an image was JPEG compressed before resampling, existing techniques detect tampering using the artifacts left by the pre-compression. However, state-of-the-art approaches cannot detect resampling in re-compressed images initially compressed with high quality factor. In this paper, we propose a novel deep learning approach to adaptively learn resampling detection features directly from data. To accomplish this, we use our recently proposed constrained convolutional layer. Through a set of experiments we evaluate the effectiveness of our proposed constrained convolutional neural network (CNN) to detect resampling in re-compressed images. The results of these experiments show that our constrained CNN can accurately detect resampling in re-compressed images in scenarios that previous approaches are unable to detect.
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Details
- Title
- On the robustness of constrained convolutional neural networks to JPEG post-compression for image resampling detection
- Creators
- Belhassen BayarMatthew C Stamm
- Publication Details
- The Institute of Electrical and Electronics Engineers, Inc. (IEEE) Conference Proceedings, 2152
- Publisher
- The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Electrical and Computer Engineering
- Identifiers
- 991019170510304721