SSteGAN: Self-learning Steganography Based on Generative Adversarial Networks

Wang, Zihan; Gao, Neng; Wang, Xin; Qu, Xuexin; Li, Linghui

doi:10.1007/978-3-030-04179-3_22

Cited by 28 publications

(26 citation statements)

References 7 publications

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“…Consequently, exploring the optimization ability of deep learning models for adaptive and automated image watermarking is of great interest. However, compared to significant advancements on image steganography with deep neural networks [16], [17], deep learning-based image watermarking is still in its infancy.…”

Section: R Wmentioning

confidence: 99%

“…Classic Reed Solomon (RS) code [35] is adopted as the ECC to protect the information. RS (32,16) is applied to protect each row of the 32 × 16 information so that the encoded information will be a 32 × 32 watermark satisfying the fixed watermarking capacity of the proposed scheme. In the watermark, each row is a codeword with data of length 16 and a parity of length 16, and hence can correct up to an error of length 8.…”

Section: E a Case Study: Feasibility Test On Watermark Extraction Frmentioning

confidence: 99%

“…For a visual comparison, the displayed watermark extractions are the raw results before error correction. After executing RS (32,16), all the watermark extractions in the testing cases can be restored to the original information without errors, as shown in Fig. 14.…”

Section: E a Case Study: Feasibility Test On Watermark Extraction Frmentioning

confidence: 99%

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An Automated and Robust Image Watermarking Scheme Based on Deep Neural Networks

Zhong

Huang

Mastorakis

et al. 2021

IEEE Trans. Multimedia

103

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Digital image watermarking is the process of embedding and extracting a watermark covertly on a cover-image. To dynamically adapt image watermarking algorithms, deep learning-based image watermarking schemes have attracted increased attention during recent years. However, existing deep learning-based watermarking methods neither fully apply the fitting ability to learn and automate the embedding and extracting algorithms, nor achieve the properties of robustness and blindness simultaneously. In this paper, a robust and blind image watermarking scheme based on deep learning neural networks is proposed. To minimize the requirement of domain knowledge, the fitting ability of deep neural networks is exploited to learn and generalize an automated image watermarking algorithm. A deep learning architecture is specially designed for image watermarking tasks, which will be trained in an unsupervised manner to avoid human intervention and annotation. To facilitate flexible applications, the robustness of the proposed scheme is achieved without requiring any prior knowledge or adversarial examples of possible attacks. A challenging case of watermark extraction from phone camera-captured images demonstrates the robustness and practicality of the proposal. The experiments, evaluation, and application cases confirm the superiority of the proposed scheme.

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Section: R Wmentioning

confidence: 99%

Section: E a Case Study: Feasibility Test On Watermark Extraction Frmentioning

confidence: 99%

See 1 more Smart Citation

An Automated and Robust Image Watermarking Scheme Based on Deep Neural Networks

Zhong

Huang

Mastorakis

et al. 2021

IEEE Trans. Multimedia

103

View full text Add to dashboard Cite

show abstract

“…Unsupervised GANs were introduced to avoid expert knowledge and complex artificial rules required for steganography and payload capacity by generating the steganographic image from the secret message without the cover image [68]. Further, GANs based method was proposed for hiding the binary data inside an image successfully [69].…”

Section: H Deep Learning For Steganalysis and Steganographymentioning

confidence: 99%

Deep Learning for Cyber Security Applications: A Comprehensive Survey

Ravi¹,

Alazab²,

Srinivasan³

et al. 2021

Preprint

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Deep Learning (DL), a novel form of machine learning (ML) is gaining much research interest due to its successful application in many classical artificial intelligence (AI) tasks as compared to classical ML algorithms (CMLAs). Recently, DL architectures are being innovatively modelled for diverse applications in the area of cyber security. The literature is now growing with DL architectures and their variations for exploring different innovative DL models and prototypes that can be tailored to suit specific cyber security applications. However, there is a gap in literature for a comprehensive survey reporting on such research studies. Many of the survey-based research have a focus on specific DL architectures and certain types of malicious attacks within a limited cyber security problem scenario of the past and lack futuristic review. This paper aims at providing a well-rounded and thorough survey of the past, present, and future DL architectures including next-generation cyber security scenarios related to intelligent automation, Internet of Things (IoT), Big Data (BD), Blockchain, cloud and edge technologies. <br>This paper presents a tutorial-style comprehensive review of the state-of-the-art DL architectures for diverse applications in cyber security by comparing and analysing the contributions and challenges from various recent research papers. Firstly, the uniqueness of the survey is in reporting the use of DL architectures for an extensive set of cybercrime detection approaches such as intrusion detection, malware and botnet detection, spam and phishing detection, network traffic analysis, binary analysis, insider threat detection, CAPTCHA analysis, and steganography. Secondly, the survey covers key DL architectures in cyber security application domains such as cryptography, cloud security, biometric security, IoT and edge computing. Thirdly, the need for DL based research is discussed for the next generation cyber security applications in cyber physical systems (CPS) that leverage on BD analytics, natural language processing (NLP), signal and image processing and blockchain technology for smart cities and Industry 4.0 of the future. Finally, a critical discussion on open challenges and new proposed DL architecture contributes towards future research directions.

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“…DNN based secret information removal is studied by [109]. Unsupervised GANs was introduced to avoid lot of expert knowledge and complex artificial rules in steganography [110]. The method generates the stego image from the secret message without the cover image.…”

Section: H Deep Learning For Steganalysis and Steganographymentioning

confidence: 99%