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Image operator forensics and sequence estimation using robust deep neural network
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DOI：10.1007/s11042-023-17389-0.png)
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Digital images can be manipulated with recent tools. Image forensics examines the image from several angles to spot any anomalies. Most techniques are applicable to detect a single operation on the image. In actual practice, fake photos are manipulated with multiple operations and compression algorithms. A convolutional neural network with a reasonable size is designed to detect operators and the respective sequences for two operators in particular. The bottleneck strategy is incorporated to optimize the network training cost and a high-depth network. The detection of a particular operator depends on inherent statistical information. A single global average pooling layer preserves the statistical information in a convolutional neural network. The strength of existing detection techniques is also reduced in low-resolution and high-compression environments. The proposed method performs better than existing techniques on compressed small-size images even though forensic is difficult in small-size and compressed images due to inadequate statistical traces. The proposed convolutional neural network also applies to detect operators with unknown specifications and compression not used in training.
Keyword:
Image forgery detection
Image operator sequence
Convolutional neural network
Image filtering
Image forensics
期刊
IF:
3
论文数:
1.9W
被引数:
3.2W
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