Double and triple compression-based forgery detection in JPEG images using deep convolutional neural network

Bakas, Jamimamul; Ramachandra, Sumana; Ruchira, Naskar

doi:10.1117/1.jei.29.2.023006

Cited by 7 publications

(5 citation statements)

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“…Some application scenarios, such as video call or real-time image transmission, have high requirements for real-time. Therefore, the image compression algorithm needs to be able to complete the compression and decompression operations as quickly as possible while ensuring the image quality 9 . Mobile devices usually have limited computing and storage resources.…”

Section: Compression Principle Of Mobile Communication Image Transmis...mentioning

confidence: 99%

Design of mobile communication image transmission compression algorithm based on convolutional neural network

Zhu

2024

Second International Conference on Physics, Photonics, and Optical Engineering (ICPPOE 2023)

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With the popularization of mobile communication applications, image transmission plays an increasingly important role in communication. However, due to the limitation of communication bandwidth and storage resources, efficient image compression has become an urgent need. The purpose of this study is to design an innovative mobile communication image transmission compression algorithm by introducing the deep learning technology of convolutional neural network (CNN). In this algorithm, through the feature learning of convolution level and the hierarchical structure of CNN, the abstract features in the image are gradually extracted. This feature extraction process can not only capture the texture and structure information of the image effectively, but also has the advantage of resisting the common information loss problem in traditional compression methods. In order to enhance the learning ability of the network, we introduce residual connection, which improves the network's long-range dependence and attention to important areas. Experimental results show that compared with traditional compression methods, the algorithm based on CNN can maintain higher image quality at the same compression rate. The algorithm is more universal and can meet the needs of mobile communication image transmission in different types and scenarios. In addition, the impact on transmission quality is relatively small, and it can maintain good visual effects even at high compression rate.

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Section: Compression Principle Of Mobile Communication Image Transmis...mentioning

confidence: 99%

Design of mobile communication image transmission compression algorithm based on convolutional neural network

Zhu

2024

Second International Conference on Physics, Photonics, and Optical Engineering (ICPPOE 2023)

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“…8 used sensor noise to identify the presence of splicing operations, and the detection method based on sensor noise will fail if photos taken by the same camera are used for splicing, making the detection performance significantly degraded. The detection algorithms based on JPEG compression traces 9 – 11 can only detect spliced JPEG images. Furthermore, traditional detection methods require manual feature extraction and rely on the prior knowledge of the designer.…”

Section: Introductionmentioning

confidence: 99%

End-to-end image splicing localization based on multi-scale features and residual refinement module

Gan,

Jiang,

Chai

et al. 2023

J. Electron. Imag.

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The addition of objects to the original image in splice forgery results in a change in the semantics of the original image, and distribution of these spliced images may bring negative impacts. To solve this issue, many forgery detection methods based on convolutional neural networks are presented. However, they tend to extract deep features, but ignore the importance of shallow semantics. In addition, the complexity of the forgery localization task leads to insufficient accuracy in detecting smaller forged regions. Based on the above problems, an end-to-end image splicing localization network based on multi-scale features and a residual refinement module (RRM) is proposed in this work. Our approach can be roughly divided into two modules: the detection module and the RRM. First, shallow and deep features are extracted from a backbone network, and deep features are then processed by the deeper atrous spatial pyramid pooling (ASPP) module to extract multi-scale features. The deeper ASPP module uses a smaller dilation rate and light convolution, which is more suitable for detecting complex counterfeit images. Second, the shallow features are fused with the multi-scale features to complement the shallow semantic information, such as texture and edges, which further improve the robustness of the model. Finally, the detection network generates coarse prediction maps that are fed to the RRM, and the RRM optimizes these masks by smoothing the boundaries, filling small gaps, and enhancing the edge details, which improves the pixel-level segmentation results for forgery detection. Extensive experimental results on several public datasets show that this method outperforms other stateof-the-art methods in image forgery localization.

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“…Nevertheless, a large number of forged digital images are generated by malicious use of these techniques, which has led to a serious security and trust crisis of digital multimedia. Therefore, image forensics has gradually attracted an increasing concern in the digital multimedia era, such as JPEG compression forensics [1,2], median filtering detection [3], copy-moving and splicing localization [4,5], universal image manipulation detection [6,7], and so on.…”

Section: Introductionmentioning

confidence: 99%

“…Recently, deep convolutional neural networks (DCNNs) have made great success in many fields [18][19][20] via their powerful learning capabilities. Inspired by these works, some researchers have made some attempts to develop CNN-based forensics methods, such as median filtering forensics [3], camera model identification [21], copy-move and splicing localization [4], as well as JPEG compression forensics [2]. A few research efforts have been also devoted to deep learning-based forensics for image inpainting [22,23].…”

Section: Introductionmentioning

confidence: 99%

A Frequency Attention-Based Dual-Stream Network for Image Inpainting Forensics

et al. 2023

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The rapid development of digital image inpainting technology is causing serious hidden danger to the security of multimedia information. In this paper, a deep network called frequency attention-based dual-stream network (FADS-Net) is proposed for locating the inpainting region. FADS-Net is established by a dual-stream encoder and an attention-based blue-associative decoder. The dual-stream encoder includes two feature extraction streams, the raw input stream (RIS) and the frequency recalibration stream (FRS). RIS directly captures feature maps from the raw input, while FRS performs feature extraction after recalibrating the input via learning in the frequency domain. In addition, a module based on dense connection is designed to ensure efficient extraction and full fusion of dual-stream features. The attention-based associative decoder consists of a main decoder and two branch decoders. The main decoder performs up-sampling and fine-tuning of fused features by using attention mechanisms and skip connections, and ultimately generates the predicted mask for the inpainted image. Then, two branch decoders are utilized to further supervise the training of two feature streams, ensuring that they both work effectively. A joint loss function is designed to supervise the training of the entire network and two feature extraction streams for ensuring optimal forensic performance. Extensive experimental results demonstrate that the proposed FADS-Net achieves superior localization accuracy and robustness on multiple datasets compared to the state-of-the-art inpainting forensics methods.

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Double and triple compression-based forgery detection in JPEG images using deep convolutional neural network

Cited by 7 publications

References 0 publications

Design of mobile communication image transmission compression algorithm based on convolutional neural network

Design of mobile communication image transmission compression algorithm based on convolutional neural network

End-to-end image splicing localization based on multi-scale features and residual refinement module

A Frequency Attention-Based Dual-Stream Network for Image Inpainting Forensics

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