A Generative Steganography Method Based on WGAN-GP

Li, Jun; Niu, Ke; Liao, Lili; Wang, Lijie; Liu, Jia; Yu, Lei; Zhang, Minqing

doi:10.1007/978-981-15-8083-3_34

Cited by 30 publications

(18 citation statements)

References 11 publications

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“…Deep convolutional generative adversarial network (DCGAN) [27] is a commonly upgraded version of GAN, which applies the convolution neural network to the design of the generator and discriminator to improve the quality of the generated sample, as well as the training speed. Recently, some different methods [28]- [31] using GAN for image steganography have been proposed to improve the ability to defend against steganalysis.…”

Section: A Image Steganography Via Ganmentioning

confidence: 99%

“…Table 3 shows the comparison results between the proposed extractor and two related works [29], [31]. The extractor in method [29] was trained through the generator via DCGAN, the extractor in method [31] was based on the WGAN, and the proposed is based on the PGGAN. Compared with other methods, the main different properties of the proposed extractor are independence and authentication.…”

Section: Training and Analysis Of The Extractormentioning

confidence: 99%

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Steganographic Secret Sharing With GAN-Based Face Synthesis and Morphing for Trustworthy Authentication in IoT

2021

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Trust and security are fundamental to the successful adoption of the Internet of Things (IoT). This paper proposes a secure message authentication scheme based on steganographic secret sharing for building trust in IoT systems. In our scheme, the message is split and distributed to two participants by a dealer, and it can be revealed only when the two authorized participants grant their consents. Neither of the participants can disclose the message without the consent of the other. To avoid malicious cyberattacks in IoT communications, each share of message is concealed in the form of a human face image, referred to as the shadow image, via a generative adversarial network (GAN). For each participant, a convolutional neural network (CNN) is trained to extract the share of message from the shadow image generated with the participant's key. Distortions and alterations to the shadow images may occur during the transmission from the dealer to the participant. As a tamper-evident design, each shadow image is morphed with the participant's source image under the participant's customized morphing parameter. Cheater detection is also crucial for involved participants to identify fake shadow images during the secret retrieval process. As a cheating countermeasure, the shadow image for one participant is morphed with that for the other and then morphed further with the given source image. The proposed scheme enables multi-factor authentication in the sense that the message is protected by the keys, source images, and morphing parameters of two participants.

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Section: A Image Steganography Via Ganmentioning

confidence: 99%

Section: Training and Analysis Of The Extractormentioning

confidence: 99%

Steganographic Secret Sharing With GAN-Based Face Synthesis and Morphing for Trustworthy Authentication in IoT

2021

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“…The idea is to use bits of the secret message to directly sample from the probability distribution of the generative model. The receiver can then recover the message by examining the steganographic image [103,137,143] or audio sample [37,260] and inverting the choices made in the generation of the sample.…”

Section: Steganography With Generative Modelsmentioning

confidence: 99%

Generative Models for Security: Attacks, Defenses, and Opportunities

Bauer¹,

Bindschaedler²

2021

Preprint

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Generative models learn the distribution of data from a sample dataset and can then generate new data instances. Recent advances in deep learning has brought forth improvements in generative model architectures, and some state-of-the-art models can (in some cases) produce outputs realistic enough to fool humans.We survey recent research at the intersection of security and privacy and generative models. In particular, we discuss the use of generative models in adversarial machine learning, in helping automate or enhance existing attacks, and as building blocks for defenses in contexts such as intrusion detection, biometrics spoofing, and malware obfuscation. We also describe the use of generative models in diverse applications such as fairness in machine learning, privacy-preserving data synthesis, and steganography. Finally, we discuss new threats due to generative models: the creation of synthetic media such as deepfakes that can be used for disinformation. INTRODUCTIONGenerative models learn to characterize the distribution of data using only samples from it and then generate new data instances from this distribution. Although generative models are not new, the deep learning revolution has reinvigorated research into generative model architectures, and deep generative models using state-of-the-art architectures can now produce output that is sometimes indistinguishable from real-world data. Along with this, comes a host of new issues and opportunities relating to security and privacy.This paper provides a comprehensive survey of research at the intersection of generative models and security and privacy. In particular, we describe the recent use of generative models in adversarial machine learning. We also discuss applications such as producing adversarial examples without perturbations, steganography, and privacy-preserving data synthesis. We show how a number of attacks and defenses for various cybersecurity problems such as password generation, intrusion, and malware detection, can benefit from generative models because of their ability to learn the distribution of the training data. By characterizing the data distribution, generative models help practitioners better understand phenomena they are studying and supplement existing attack and defense techniques. Finally, we discuss the extent to which deep generative models present new threats when used to produce synthetic media.The increased intensity of work at the intersection of generative models and security/privacy is evidenced by a growing body of literature. This is illustrated in Fig. 1, which shows the number of papers (published and pre-prints) on this topic from 2000 to 2020.This survey is structured as follows. Section 2 provides a brief overview of generative model architectures, as well as a discussion of metrics used to evaluate generative models. Section 3, we survey the use of generative models for intrusion detection, malware detection, and biometric spoofing. In Section 4, we describe how generative models can be used to create synthetic dat...

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“…Inspired by Hu's method, Li et al [94] propose a new framework which train the message extractor and stego image generator at the same time. WGAN-GP instead of DCGAN is adapted to generate stego image with higher visual quality.…”

Section: Steganography By Wgan-gpmentioning

confidence: 99%

Recent Advances of Image Steganography With Generative Adversarial Networks

et al. 2020

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In the past few years, the Generative Adversarial Network (GAN) which proposed in 2014 has achieved great success. GAN has achieved many research results in the field of computer vision and natural language processing. Image steganography is dedicated to hiding secret messages in digital images, and has achieved the purpose of covert communication. Recently, research on image steganography has demonstrated great potential for using GAN and neural networks. In this paper we review different strategies for steganography such as cover modification, cover selection and cover synthesis by GANs, and discuss the characteristics of these methods as well as evaluation metrics and provide some possible future research directions in image steganography.

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A Generative Steganography Method Based on WGAN-GP

Cited by 30 publications

References 11 publications

Steganographic Secret Sharing With GAN-Based Face Synthesis and Morphing for Trustworthy Authentication in IoT

Steganographic Secret Sharing With GAN-Based Face Synthesis and Morphing for Trustworthy Authentication in IoT

Generative Models for Security: Attacks, Defenses, and Opportunities

Recent Advances of Image Steganography With Generative Adversarial Networks

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