SSDeN: Framework for Screen-Shooting Resilient Watermarking via Deep Networks in the Frequency Domain

Bai, Rui; Li, Li; Zhang, Shanqing; Lu, Jianfeng; Chang, Chin‐Chen

doi:10.3390/app12199780

Cited by 11 publications

(5 citation statements)

References 37 publications

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“…The third row represents the difference images between the watermarked image and the noisy image. The residual diagram I re between watermarked image and noisy images can be calculated from Equation (7) as follows:…”

Section: Network Training Speed Comparisonmentioning

confidence: 99%

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Anti-Screenshot Watermarking Algorithm for Archival Image Based on Deep Learning Model

Wei

Chang

Bai

et al. 2023

Entropy

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Over recent years, there are an increasing number of incidents in which archival images have been ripped. Leak tracking is one of the key problems for anti-screenshot digital watermarking of archival images. Most of the existing algorithms suffer from low detection rate of watermark, because the archival images have a single texture. In this paper, we propose an anti-screenshot watermarking algorithm for archival images based on Deep Learning Model (DLM). At present, screenshot image watermarking algorithms based on DLM can resist screenshot attacks. However, if these algorithms are applied on archival images, the bit error rate (BER) of the image watermark will increase dramatically. Archival images are ubiquitous, so in order to improve the robustness of archival image anti-screenshot, we propose a screenshot DLM “ScreenNet”. It aims to enhance the background and enrich the texture with style transfer. Firstly, a preprocessing process based on style transfer is added before the insertion of an archival image into the encoder to reduce the influence of the screenshot process of the cover image. Secondly, the ripped images are usually moiréd, so we generate a database of ripped archival images with moiréd by means of moiréd networks. Finally, the watermark information is encoded/decoded through the improved ScreenNet model using the ripped archive database as the noise layer. The experiments prove that the proposed algorithm is able to resist anti-screenshot attacks and achieves the ability to detect watermark information to leak the trace of ripped images.

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Section: Network Training Speed Comparisonmentioning

confidence: 99%

“…DLM can find the feature points that contain the image watermark in the case of screenshot attack on the image. Additionally, Li et al [7] added an anti-screenshot noise layer to the end-to-end DLM. The noise layer is presented between the encoder and decoder and used to simulate possible attacks on watermarked images.…”

Section: Introductionmentioning

confidence: 99%

Anti-Screenshot Watermarking Algorithm for Archival Image Based on Deep Learning Model

Wei

Chang

Bai

et al. 2023

Entropy

Self Cite

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“…Transform Domain: Bai et al [139] introduced a separable noise layer over the DCT domain in the embedding and extraction layers to simulate screen-shooting attacks. SSIMbased loss functions were introduced to improve imperceptibility.…”

Section: Specific Decoder Trainingmentioning

confidence: 99%

Review of Image Forensic Techniques Based on Deep Learning

et al. 2023

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Digital images have become an important carrier for people to access information in the information age. However, with the development of this technology, digital images have become vulnerable to illegal access and tampering, to the extent that they pose a serious threat to personal privacy, social order, and national security. Therefore, image forensic techniques have become an important research topic in the field of multimedia information security. In recent years, deep learning technology has been widely applied in the field of image forensics and the performance achieved has significantly exceeded the conventional forensic algorithms. This survey compares the state-of-the-art image forensic techniques based on deep learning in recent years. The image forensic techniques are divided into passive and active forensics. In passive forensics, forgery detection techniques are reviewed, and the basic framework, evaluation metrics, and commonly used datasets for forgery detection are presented. The performance, advantages, and disadvantages of existing methods are also compared and analyzed according to the different types of detection. In active forensics, robust image watermarking techniques are overviewed, and the evaluation metrics and basic framework of robust watermarking techniques are presented. The technical characteristics and performance of existing methods are analyzed based on the different types of attacks on images. Finally, future research directions and conclusions are presented to provide useful suggestions for people in image forensics and related research fields.

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“…Liu [114] Zhang [122] Jia [117] Ma [120] Zhong [123] Chen [142] Xu [143] Two-stage Luo [144] Zhang [145] One-stage End-to-end Screen-shooting Mellimi [109] Zhang [122] Zhong [123] Wang [124] Zhang [119] Fang [125] Improved Distoration Simulation Jia [135] Fang [136] Yoo [137] Tancik [138] Fang [126] Pramila [129] Gugelmann [130] Dong [131] Bai [132] Lu [133] Boujerfaoui [134] Fang [127] Transform Domain…”

Section: Specific Decoder Trainingmentioning

confidence: 99%

“…Transform Domain: Bai et al [132] introduced a separable noise layer over the DCT domain in the embedding and extraction layers to simulate screen-shooting attacks. SSIM-based loss functions was introduced to improve imperceptibility.…”

Section: Specific Decoder Trainingmentioning

confidence: 99%

Review on Image Forensic Techniques Based on Deep Learning

Shi¹,

Chen²,

Wang³

et al. 2023

Preprint

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Digital images have become an important carrier for people to access information in the information age. However, with the development of the technology, digital images are vulnerable to illegal access and tampering, to the extent that they pose a serious threat to personal privacy, social order and national security. Therefore, image forensic techniques have become an important research topic in the field of multimedia information security. In recent years, deep learning technology has been widely applied in the field of image forensics and the performance achieved has significantly exceeded the conventional forensic algorithms. This survey compares the state-of-the-art image forensic techniques based on deep learning in recent years. The image forensic techniques are divided into passive and active forensics. In passive forensics, forgery detection techniques are reviewed, and the basic framework, evaluation metrics and commonly used datasets for forgery detection are presented. The performance, advantages and disadvantages of existing methods are also compared and analyzed according to different types of detection. In active forensics, robust image watermarking techniques are overviewed, the evaluation metrics and basic framework of robust watermarking techniques are presented. The technical characteristics and performance of existing methods are analyzed based on the different types of attacks on images. Finally, future research directions and conclusions are given to provide useful suggestions for people in image forensics and related research fields.

show abstract

SSDeN: Framework for Screen-Shooting Resilient Watermarking via Deep Networks in the Frequency Domain

Cited by 11 publications

References 37 publications

Anti-Screenshot Watermarking Algorithm for Archival Image Based on Deep Learning Model

Anti-Screenshot Watermarking Algorithm for Archival Image Based on Deep Learning Model

Review of Image Forensic Techniques Based on Deep Learning

Review on Image Forensic Techniques Based on Deep Learning

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