The rapid advancement of computer image production technology presents a grave peril to the trustworthiness of digital images, necessitating a great practical requirement for research on computer generated image detection technology in the realms of digital forensics and judicial assessment. Therefore, we propose a deep local binary pattern network (DLBPNet) to detect computer-generated image (CGI). Specifically, we first designed a deep local binary pattern module, which has three parallel branches, each of which utilizes a 1 × 1 convolution layer to learn the correlation between color channels, a learnable pre-processing filter to eliminate information redundancy, and an LBP submodule to extract low-level discriminative features. Then we feed the output of this module into successive generalized central difference convolution modules to further learn the higher-level hierarchical representation for making decision. Our proposed DLBP-Net network was confirmed to be effective in both detection accuracy and generalization ability through extensive experiments, which yielded detection accuracy of 94.35% on SPL2018 dataset, 94.03% on DSToK dataset, and 93.87% on the mixed dataset.
With the explosive advancements in image display technology, recapturing high-quality images from high-fidelity LCD screens becomes more and more easy. Such recaptured images can not only be used for deceiving intelligent recognition systems but also for hiding tampering traces. In order to prevent such a security loophole, we propose a recaptured image detection approach based on deep hybrid correlation network. Specifically, we first design a deep hybrid correlation module to extract the correlations in different color channels and neighboring pixels. This module has three different branches, in which a 1×1 convolution layer is used to learn the correlations between color channels while two consecutive convolution sub-modules are used to extract the correlations between neighboring pixels. Then we feed the output of this module into consecutive convolution modules to further learn the hierarchical representation for make decision. Ablation experiments verify the effectiveness of our proposed deep hybrid correlation module, while single database experiments demonstrate that our proposed method can achieve average accuracy with about 99% on three public databases. Specifically, our method not only performs very close to the state-of-the-art methods on the most difficult-to-detect ICL-COMMSP database and the relative low-quality NTU-ROSE database, but also improves the performance on the most diverse Dartmouth database obviously, which verifies the effectiveness of the proposed deep architecture. Besides, mixed database experiments verify the superiority of the generalization ability of our proposed method.
With the widespread usage of sophisticated image editing software, digital image tampering becomes very convenient and easy, which makes the detection of image authenticity significant. Among various image forensic tools, double JPEG image compression detector, which is not sensitive to specific tampering operation, has received large attention. In this paper, we propose an improved double JPEG compression detection method based on noise-free DCT coefficients histograms. Specifically, we eliminate the quantization noise which introduced by decompression and rounding operation before estimating the periodicity of the quantized DCT coefficients histograms. Then, a block-wise posterior probability map can be obtained from the estimated periodicity to expose the suspect regions. Extensive experimental results in both quantitative and qualitative terms prove the superior of our proposed method when compared with the related methods.
With the great development of image display technology and the widespread use of various image acquisition device, recapturing high-quality images from high-fidelity LCD (liquid crystal display) screens becomes relative convenient. These recaptured images pose serious threats on image forensic technologies and bio-authentication systems. In order to prevent the security loophole of image recapture attack, inspired by the effectiveness of LBP (local binary pattern) on recaptured image detection and the satisfactory performance of deep learning techniques on many image forensics tasks, we propose a recaptured image detection method based on convolutional neural networks with local binary patterns coding. The LBP coded maps are extracted as the input of the proposed convolutional neural networks architecture. Extensive experiments on two public high-quality recaptured image databases under two different scenarios demonstrate the superior of our designed method when compared with the state-of-the-art approaches.
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