An Adaptive Visible Watermark Embedding Method based on Region Selection

Qi, Wenfa; Liu, Yuxin; Guo, Sirui; Wang, Xiang

doi:10.1155/2021/6693343

Cited by 9 publications

(5 citation statements)

References 33 publications

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“…Watermarks can be classified into several classes in terms of the monitored parameters. In terms of visual detection, we divide watermarks into visible [33,34] and invisible [35,36] watermarks. In terms of robustness to image transformations, we distinguish fragile watermarks, semi-fragile watermarks and robust watermarks.…”

Section: Authentication Of Images Based On Data Hidingmentioning

confidence: 99%

Steganography Approach to Image Authentication Using Pulse Coupled Neural Network

Forgáč,

Očkay,

Javurek

et al. 2023

cai

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This paper introduces a model for the authentication of large-scale images. The crucial element of the proposed model is the optimized Pulse Coupled Neural Network. This neural network generates position matrices based on which the embedding of authentication data into cover images is applied. Emphasis is placed on the minimalization of the stego image entropy change. Stego image entropy is consequently compared with the reference entropy of the cover image. The security of the suggested solution is granted by the neural network weights initialized with a steganographic key and by the encryption of accompanying steganographic data using the AES-256 algorithm. The integrity of the images is verified through the SHA-256 hash function. The integration of the accompanying and authentication data directly into the stego image and the authentication of the large images are the main contributions of the work.

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Section: Authentication Of Images Based On Data Hidingmentioning

confidence: 99%

Steganography Approach to Image Authentication Using Pulse Coupled Neural Network

Forgáč,

Očkay,

Javurek

et al. 2023

cai

View full text Add to dashboard Cite

show abstract

“…• [9] suggested an adaptive method for embedding visible watermarking in order to address the concerns about robustness, visibility, and transparency that afflicted some watermarking systems. Initially, super-pixel detection is used to detect protruding regions in the host image.…”

Section: Related Workmentioning

confidence: 99%

A Double Embedding of an Anti-Removable Visible Logo Using Discrete Wavelet Transform and Chaotic Map

Resen,

Laftah

2023

Iraqi Journal of Science

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The aims of visible watermarking are to prevent the illegal copying of videos and images and to avoid the theft and unauthorized advertisement of the legitimate owners' content. The visible watermark is distinguishable with the naked eye. This makes it easily removed by an attacker. This paper proposes an anti-removable visible logo by double embedding using the discrete wavelet transform (DWT) and a chaotic map. The main purpose of this method is to prevent the removal attack and restore the original video without information loss after the logo is legally removed. DWT is used for both the cover area and the logo to embed sub-bands of information from the cover area into sub-bands of the logo. The main idea of using a chaotic map is to make it difficult to recover the original video when the logo is removed by illegal means. The experiment results show that the proposed method is robust against illegal watermark removal. In addition, video frames were recovered without loss after the legal extraction process.

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“…e classification of forensics is shown in Figure 5. e active forensic method is to actively add a digital watermark [70,71] or digital signature [72] to the original image. When copyright and authentication problems occur, the extraction algorithm is used to extract this information to provide copyright proof or content authenticity and integrity authentication.…”

Section: Digital Image Forensicsmentioning

confidence: 99%

Overview of Image Inpainting and Forensic Technology

Liu

Bukhari

2022

Security and Communication Networks

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At present, digital image is the most significant information carrier on the Internet, which provides convenience for people to exchange information. With the progress of image processing theory, digital image inpainting technology has also developed rapidly. It not only enhances the image expression but also provides tools for image forgery. Misusing the tools may trigger a crisis of trust in the image. To eliminate the negative impact of image forgery, many scholars have conducted in-depth research on it and proposed a series of detection methods called image forensics for institutions or communities to identify such forged images. Current forensic technology still has many limitations, such as relying on specific features and data distribution which makes forensic technology lag behind image inpainting technology. So far, academia still lacks a unified understanding of image forgery and detection technology, and the research architecture of inpainting and forensic technology is not clear. This article reviews the development of image inpainting and forensic technology and systematically summarizes and scientifically classifies the current research work. Finally, the forensic technology is summarized and the future research direction is prospected to guide subsequent scholars to further promote the progress of image forensic technology.

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An Adaptive Visible Watermark Embedding Method based on Region Selection

Cited by 9 publications

References 33 publications

Steganography Approach to Image Authentication Using Pulse Coupled Neural Network

Steganography Approach to Image Authentication Using Pulse Coupled Neural Network

A Double Embedding of an Anti-Removable Visible Logo Using Discrete Wavelet Transform and Chaotic Map

Overview of Image Inpainting and Forensic Technology

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