Coverless information hiding based on the generation of anime characters

Cao, Yi; Zhou, Zhili; Wu, Q. M. Jonathan; Yuan, Chengsheng; Sun, Xingming

doi:10.1186/s13640-020-00524-4

Cited by 32 publications

(18 citation statements)

References 29 publications

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“…Table 1 compares the hiding capacity of the proposed system with other systems. [20] 1 4 Baker et al [4] 1 ∼15 Zhou et al [21] 1 6 Zheng et al [17] 1 8 Tan et al [7] 3 2 Cao et al [22] 3 6 Cao et al [23] 6 8 Zou et al [24] 8 0 Zhou et al [16] 384 Saad et al [8] 760 Luo et al [25] 800 The proposed system 3432…”

Section: Hiding Capacitymentioning

confidence: 99%

A New Generative Mathematical Model for Coverless Steganography System Based on Image Generation

Seddik¹,

Salah²,

Behery³

et al. 2023

Computers, Materials &Amp; Continua

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The ability of any steganography system to correctly retrieve the secret message is the primary criterion for measuring its efficiency. Recently, researchers have tried to generate a new natural image driven from only the secret message bits rather than using a cover to embed the secret message within it; this is called the stego image. This paper proposes a new secured coverless steganography system using a generative mathematical model based on semi Quick Response (QR) code and maze game image generation. This system consists of two components. The first component contains two processes, encryption process, and hiding process. The encryption process encrypts secret message bits in the form of a semi-QR code image whereas the hiding process conceals the pregenerated semi-QR code in the generated maze game image. On the other hand, the second component contains two processes, extraction and decryption, which are responsible for extracting the semi-QR code from the maze game image and then retrieving the original secret message from the extracted semi-QR code image, respectively. The results were obtained using the bit error rate (BER) metric. These results confirmed that the system achieved high hiding capacity, good performance, and a high level of robustness against attackers compared with other coverless steganography methods.

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Section: Hiding Capacitymentioning

confidence: 99%

A New Generative Mathematical Model for Coverless Steganography System Based on Image Generation

Seddik¹,

Salah²,

Behery³

et al. 2023

Computers, Materials &Amp; Continua

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“…In addition, self-attention blocks [36] and Wasserstein loss with gradient penalty [15] are introduced to improve the performance of its generator. For further increased embedding capacity, [5] uses face images as steganography containers based on image selection and a StarGAN, while [4] generates images of anime characters with specific attributes such as hair colour, which represent specific binary sequences based on a codebook, for enhanced extraction stability.…”

Section: Steganography Without Embeddingmentioning

confidence: 99%

Image Disentanglement Autoencoder for Steganography without Embedding

Liu

et al. 2022

2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

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Conventional steganography approaches embed a secret message into a carrier for concealed communication but are prone to attack by recent advanced steganalysis tools. In this paper, we propose Image DisEntanglement Autoencoder for Steganography (IDEAS) as a novel steganography without embedding (SWE) technique. Instead of directly embedding the secret message into a carrier image, our approach hides it by transforming it into a synthesised image, and is thus fundamentally immune to typical steganalysis attacks. By disentangling an image into two representations for structure and texture, we exploit the stability of structure representation to improve secret message extraction while increasing synthesis diversity via randomising texture representations to enhance steganography security. In addition, we design an adaptive mapping mechanism to further enhance the diversity of synthesised images when ensuring different required extraction levels. Experimental results convincingly demonstrate IDEAS to achieve superior performance in terms of enhanced security, reliable secret message extraction and flexible adaptation for different extraction levels, compared to state-of-the-art SWE methods.

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“…So, the coverless data hiding concept has been proposed [5]. It does not mean that cover is not required [6], or sensitive data can be transmitted with no cover medium. Instead, the embedding of the payload is achieved by carriers generation or by mapping a secret message with it [7].…”

Section: Coverless Steganographymentioning

confidence: 99%

Hacking Anti-Shoplifting System to Hide Data within Clothes

Saad¹,

Hussam²,

Zubair³

2021

Computers, Materials &Amp; Continua

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Steganography has been used to prevent unauthorized access to private information during transmission. It is the scheme of securing sensitive information by concealing it within carriers such as digital images, videos, audio, or text. Current steganography methods are working by assigning a cover le then embed the payload within it by making some modi cations, creating the stego-le. However, the left traces that are caused by these modi cations will make steganalysis algorithms easily detect the hidden payload. Aiming to solve this issue, a novel, highly robust steganography method based on hacking anti-shoplifting systems has proposed to hide data within clothes. The anti-Shoplifting system is an anti-theft security system that protects goods and products, leaving the store in an illegal way (i.e., without paying for them). The proposed method works by modifying the default anti-shoplifting system by changing its built-in soft RFID (radio-frequency identi cation) tags sewn in clothes into NFC (Near Field Communication) tags. These NFC tags are smart tags that can communicate with NFC-Enabled smart-phones using NDEF (NFC Data Exchange Format). NDEF is one of the advancements added to RFID technology by NFC, which allows the data exchange. Every NDEF message has one/more NDEF records that contain record type, a unique ID, a length, and a payload of data that contains the secret message content that can be any type of data that ts in a byte stream. Based on NDEF and NFC-enabled smart-phones, the proposed method will take the secret message from the sender, make use of his NFC-enabled smart-phone to communicate with the NFC tag, then hide the secret message within the NDEF's payload of the NFC tag stuck in clothes. Finally, to evaluate the proposed method, it has been compared with default (digital) steganography weak points. Such as time, lockable, robustness, attacks, capacity, and a few more points. The results and comparisons showed that the proposed method is more ef cient than default (digital) steganography and has many advantages.

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Coverless information hiding based on the generation of anime characters

Cited by 32 publications

References 29 publications

A New Generative Mathematical Model for Coverless Steganography System Based on Image Generation

A New Generative Mathematical Model for Coverless Steganography System Based on Image Generation

Image Disentanglement Autoencoder for Steganography without Embedding

Hacking Anti-Shoplifting System to Hide Data within Clothes

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