Information Hiding and Its Criteria for Evaluation

Iwamura, Keiichi; Kawamura, Masaki; Kuribayashi, Minoru; Iwata, Mamoru; Kang, Hyunho; Gohshi, Seiichi; Nishimura, Akira

doi:10.1587/transinf.2016mui0001

Cited by 6 publications

(8 citation statements)

References 12 publications

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“…Assessment of data hiding depends on the features of the algorithms themselves and the other factors such as the aim, implementation, and hiding environment [1,[9][10][11][12][13][14][15][16][17]. In [1,9,14], nearly a dozen of attributes are considered for characterizing data hiding techniques.…”

Section: Attributes Of Data Hiding Techniquesmentioning

confidence: 99%

“…In this approach, quantum circuits are used to calculate the PVDs of three pairs of pixels in each of the 2 × 2 blocks of the quantum cover image. The respective blocks are classified into lower level (or smooth) and higher level (or edge) blocks depending in which of six ranges the obtained PVD falls, i.e., R1=[0, 7], R2= [8,15], R3= [16,31], R4= [32,63], R5= [64,127], and R6= [128,255]. The lower level consists of R1 and R2, the higher level includes the remaining four ranges.…”

Section: Quantum Domainmentioning

confidence: 99%

“…Similarly, IHWT is applied to the Y, U, and V components of the cover frames in [233]. A secret message is encoded using a BCH (15,11) code to improve the reliability of the algorithm. The encoded secret message is XORed with a sequence of random numbers produced by a key.…”

Section: Raw Datamentioning

confidence: 99%

See 2 more Smart Citations

LSB Data Hiding in Digital Media: A Survey

Tran¹,

Zepernick²,

Chu³

2022

EAI Endorsed Transactions on Industrial Networks and Intelligen

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Data hiding or information hiding is a prominent class of information security that aims at reliably conveying secret data embedded in a cover object over a covert channel. Digital media such as audio, image, video, and three-dimensional (3D) media can act as cover objects to carry such secret data. Digital media security has acquired tremendous significance in recent years and will be even more important with the development and delivery of new digital media over digital communication networks. In particular, least significant bit (LSB) data hiding is easy to implement and to combine with other hiding techniques, offers high embedding capacity for data, can resist steganalysis and several types of attacks, and is well suited for real-time applications. This article provides a comprehensive survey on LSB data hiding in digital media. The fundamental concepts and terminologies used in data hiding are reviewed along with a general data hiding model. The five attributes of data hiding, i.e., capacity, imperceptibility, robustness, detectability, and security, and the related performance metrics used in this survey to compare the characteristics of the different LSB data hiding methods are discussed. Given the classification of data hiding methods with respect to audio, image, video, and 3D media, a comprehensive survey of LSB data hiding for each of these four digital media is provided. In particular, landmark studies, state-of-the-art approaches, and applications of LSB data hiding are described for each of the four digital media. Their performance is compared with respect to the data hiding attributes which illustrates benefits and drawbacks of the reviewed LSB data hiding methods. The article concludes with summarizing main findings and suggesting directions for future research. This survey will be helpful for researchers and practitioners to keep abreast about the potential of LSB data hiding in digital media and to develop novel applications based on suitable performance trade-offs between data hiding attributes.

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Section: Attributes Of Data Hiding Techniquesmentioning

confidence: 99%

Section: Quantum Domainmentioning

confidence: 99%

See 1 more Smart Citation

LSB Data Hiding in Digital Media: A Survey

Tran¹,

Zepernick²,

Chu³

2022

EAI Endorsed Transactions on Industrial Networks and Intelligen

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show abstract

“…Digital watermarking is used to prevent individuals from illegally using digital content, e.g., images, movies, and audio data, and also to identify unauthorized users [1]. Watermarking works by embedding secret information into contents imperceptibly [2]. In the case of image watermarking, the host image is called an original image and the marked image is called a stego-image.…”

Section: Introductionmentioning

confidence: 99%

Neural Watermarking Method Including an Attack Simulator against Rotation and Compression Attacks

Hamamoto

Kawamura

2020

IEICE Trans. Inf. & Syst.

Self Cite

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We have developed a digital watermarking method that use neural networks to learn embedding and extraction processes that are robust against rotation and JPEG compression. The proposed neural networks consist of a stego-image generator, a watermark extractor, a stegoimage discriminator, and an attack simulator. The attack simulator consists of a rotation layer and an additive noise layer, which simulate the rotation attack and the JPEG compression attack, respectively. The stego-image generator can learn embedding that is robust against these attacks, and also, the watermark extractor can extract watermarks without rotation synchronization. The quality of the stego-images can be improved by using the stego-image discriminator, which is a type of adversarial network. We evaluated the robustness of the watermarks and image quality and found that, using the proposed method, high-quality stego-images could be generated and the neural networks could be trained to embed and extract watermarks that are robust against rotation and JPEG compression attacks. We also showed that the robustness and image quality can be adjusted by changing the noise strength in the noise layer.

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“…In addition, the robustness criteria systems and platforms, which defined a variety of sample data, attacking types (including image cropping attack), attacking strength and other parameters, have been studied and published. These criteria systems, including “Stirmark” [ 25 , 26 ], Watermark Evaluation Testbed (WET) [ 27 ] and Information Hiding and its Criteria for Evaluation (IHC) [ 28 ], strive to extend the range of experimental parameters and conditions to more accurately estimate the robustness of the watermarking algorithms. For example, resistance to image cropping attack is considered as the most basic and low-level type of robustness in the Stirmark Benchmark evaluation system [ 26 ], and image databases with strategies of repeated tests have been adopted [ 29 ].…”

Section: Introductionmentioning

confidence: 99%

A Computational Model of Watermark Algorithmic Robustness Capable of Resisting Image Cropping for Remote Sensing Images

Tong

Ren

Shi

et al. 2018

Sensors

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Various watermarking algorithms have been studied to better enable the copyright protection of remote sensing images. The robustness of such algorithms against image cropping attacks has subsequently been verified mainly by various experiments. However, to date, the experimental results are subject to the differences in experimental factors and computational resource costs. Hence, the study presented in this paper proposes a robustness computation model of watermarking remote sensing images in terms of the image cropping attack. The robustness computation model consists of three parts: analysis principles, an evaluation index, and a computation method. The robustness analysis principles are provided based on the salient features of watermarking remote sensing images and attacking properties. A probability-based evaluation index is then defined to more comprehensively measure the robustness of different algorithms. The computation method developed in this study is based on permutations and the inclusion-exclusion principle to theoretically calculate robustness. The experiments conducted to verify the effectiveness of the computation model, revealed true closeness between both the calculated and experimental results. Finally, the relationships between the robustness and the different parameters used in the watermarking algorithms are investigated by using the proposed computation model.

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Information Hiding and Its Criteria for Evaluation

Cited by 6 publications

References 12 publications

LSB Data Hiding in Digital Media: A Survey

LSB Data Hiding in Digital Media: A Survey

Neural Watermarking Method Including an Attack Simulator against Rotation and Compression Attacks

A Computational Model of Watermark Algorithmic Robustness Capable of Resisting Image Cropping for Remote Sensing Images

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