Robust Median Filtering Forensics Using Image Deblocking and Filtered Residual Fusion

Median filtering (MF) is frequently applied to conceal the traces of forgery and therefore can provide indirect forensic evidence of tampering when investigating composite images. The existing MF forensic methods, however, ignore how JPEG compression affects median filtered images, resulting in heav...

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Bibliographic Details
Published in:IEEE access 2019, Vol.7, p.17174-17183
Main Authors: Shan, Wuyang, Yi, Yaohua, Qiu, Junying, Yin, Aiguo
Format: Article
Language:English
Subjects:
Artificial neural networks
convolutional neural networks
Feature extraction
Filtering
Fingerprint recognition
Forensic computing
Forensics
Image coding
Image compression
image deblocking
Image detection
Image filters
JPEG compression
JPEG encoders-decoders
Median filtering
Performance degradation
robust forensics
Robustness
Task analysis
Transform coding
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Summary:Median filtering (MF) is frequently applied to conceal the traces of forgery and therefore can provide indirect forensic evidence of tampering when investigating composite images. The existing MF forensic methods, however, ignore how JPEG compression affects median filtered images, resulting in heavy performance degradation when detecting filtered images stored in the JPEG format. In this paper, we propose a new robust MF forensic method based on a modified convolutional neural network (CNN). First, relying on the analysis of the influence on median filtered images caused by JPEG compression, we effectively suppress the interference using image deblocking. Second, the fingerprints left by MF are highlighted via filtered residual fusion. These two functions are fulfilled with a deblocking layer and a fused filtered residual (FFR) layer. Finally, the output of the FFR layer becomes input when extracting multiple features for further classification using a tailor-made CNN. The extensive experimental results show that the proposed method outperforms the state-of-the-art methods in both JPEG compressed and small-sized MF image detection.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2019.2894981