Image operator forensics and sequence estimation using robust deep neural network

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.