A New High Capacity Image Steganography Method Combined With Image Elliptic Curve Cryptography and Deep Neural Network

Duan, Xintao; Guo, Daidou; Liu, Nao; Li, Baoxia; Gou, Mengxiao; Qin, Chuan

doi:10.1109/access.2020.2971528

Cited by 82 publications

(41 citation statements)

References 40 publications

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“…In 2020, Xintao Duan et al [1] proposed a novel high capacity image steganography method on the basis of deep learning. The DCT was exploited to convert the concealed image, and subsequently the transformed image was encrypted through ECC to enhance the anti-detection property of the attained image.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Steganography hides huge numbers of data within a congregation media. For embedment of concealed data, the selected image is called cover or host image, where the ensuing image using embedded concealed data is termed as the stego image [1]. Steganography has three objectives: initially, a huge concealed data is needed; the expression payload indicates concealed data size which might be concealed in a host.…”

Section: Introductionmentioning

confidence: 99%

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Image Steganography for Pixel Prediction using K-nearest Neighbor

Lagrari¹

2020

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Nowadays to secure the privacy of the patient has increased more research interest during the Image steganography process. Least Significant Bit (LSB) substitute approach was widely exploited to hide the sensitive information in the conventional works. Here, each pixel was reinstated to achieve advanced privacy, other than it increased the complexity. This paper develops a new pixel prediction model-based image steganography to surmount the complication problems widespread in the conventional works. In the proposed pixel prediction model, the K-Nearest Neighbour (KNN) classifier is used to construct the prediction map that recognizes the appropriate pixels for the embedding process. Subsequently, from the medical image to extract the wavelet coefficients based on the Discrete Wavelet Transform (DWT) and embedding power and the undisclosed message is embedded into the HL wavelet band in the embedding phase. At last, from the medical image, the concealed message is extracted by using the DWT. The simulation of the proposed pixel prediction model is carried out by exploiting medical images from the BRATS database. The proposed pixel prediction model has attained maximum performance for the Peak Signal to Noise Ratio (PSNR), Structural Similarity Index (SSIM), and correlation factor, correspondingly.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Image Steganography for Pixel Prediction using K-nearest Neighbor

Lagrari¹

2020

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“…This trait is because of their success in exploiting the human visual system (HVS) [74][75][76][77], contrary to the text where a single bit change results in an erroneous character. Further, their layout provides several redundant and partisan areas such as edges that serve as regions of interest (ROI) [77][78][79] for embedding information, which most of the contributions on the subject [80][81][82] have exploited for increased payload.…”

Section: A Steganalysismentioning

confidence: 99%

Prudently Secure Information Theoretic LSB Steganography for Digital Grayscale Images

Rafat¹

2020

IJACSA

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The endangerment of online data breaches calls for exploring new and enhancing existing sneaky ways of clandestine communication to tailor those to match the present and futuristic technological and environmental needs, to which malicious intruders wouldn't have an answer. Cryptography and Steganography are the two distinct techniques that, for long, have remained priority choices for hiding vital information from the unauthorized. But the visibility of the encrypted contents makes these vulnerable to attack. Also, the recent legislative protection agreed to law enforcement authorities in Australia to sneak into pre-shared cryptographic secret keys (PSKs) shall have a devastating impact on the privacy of the people. Hence, the need of the hour is to veil in the encrypted data underneath the cover of Steganography, whose sole intent is to hide the very existence of information. This research endeavor enhances one of the most famous images Steganography technique called the Least Significant Bit (LSB) Steganography, from the security and information-theoretic standpoint by taking a known-cover and known-message attack scenario. The explicit proclamation of this research endeavor is that the security of LSB Steganography lies in inducing uncertainty at the time of bit embedding process. The test results rendered by the proposed methodology confers on the non-detectability and imperceptibility of the confidential information along with its strong resistance against LSB Steganalysis techniques.

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“…Once discovered, the attacker can use various approaches and tools such as brute force cracking to destroy the cipher text, which greatly increases the risks with secret communication. In short, encryption technology hides the content of covert communication [7,8], while steganography technology hides the "behavior" of covert communication, so steganography technology is able to provide better concealment and security [9,10]. Table 1 compares the differences between steganography and encryption in five aspects [11].…”

Section: Introductionmentioning

confidence: 99%

Steganography and Steganalysis in Voice over IP: A Review

Guo

Zhang

et al. 2021

Sensors

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The rapid advance and popularization of VoIP (Voice over IP) has also brought security issues. VoIP-based secure voice communication has two sides: first, for legitimate users, the secret voice can be embedded in the carrier and transmitted safely in the channel to prevent privacy leakage and ensure data security; second, for illegal users, the use of VoIP Voice communication hides and transmits illegal information, leading to security incidents. Therefore, in recent years, steganography and steganography analysis based on VoIP have gradually become research hotspots in the field of information security. Steganography and steganalysis based on VoIP can be divided into two categories, depending on where the secret information is embedded: steganography and steganalysis based on voice payload or protocol. The former mainly regards voice payload as the carrier, and steganography or steganalysis is performed with respect to the payload. It can be subdivided into steganography and steganalysis based on FBC (fixed codebook), LPC (linear prediction coefficient), and ACB (adaptive codebook). The latter uses various protocols as the carrier and performs steganography or steganalysis with respect to some fields of the protocol header and the timing of the voice packet. It can be divided into steganography and steganalysis based on the network layer, the transport layer, and the application layer. Recent research results of steganography and steganalysis based on protocol and voice payload are classified in this paper, and the paper also summarizes their characteristics, advantages, and disadvantages. The development direction of future research is analyzed. Therefore, this research can provide good help and guidance for researchers in related fields.

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A New High Capacity Image Steganography Method Combined With Image Elliptic Curve Cryptography and Deep Neural Network

Cited by 82 publications

References 40 publications

Image Steganography for Pixel Prediction using K-nearest Neighbor

Image Steganography for Pixel Prediction using K-nearest Neighbor

Prudently Secure Information Theoretic LSB Steganography for Digital Grayscale Images

Steganography and Steganalysis in Voice over IP: A Review

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