Hiding Data in Images Using Cryptography and Deep Neural Network

Sharma, Kanika; Aggarwal, Ashutosh; Singhania, Tanay; Gupta, Deepak; Khanna, Ashish

doi:10.33969/ais.2019.11009

Cited by 126 publications

(8 citation statements)

References 35 publications

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“…quality. In order to solve the problem of revealing the semantic information of secret images through residual analysis, in 2019, Sharma et al [13] solved the problem of revealing secret images by scrambling and encrypting secret images; different from the algorithm proposed by Sharma et al, Duan et al et al [14] performed DCT transformation on the secret image, encrypted it using an elliptic curve encryption algorithm, embedded it into the cover image, and finally generate a secret image. In 2020, Buluja further improved the scheme [15], and solved the existing security problems through the encryption method of multi-graph embedding and image scrambling.…”

Section: Information Hiding Model Based On Encoder-decoder Networkmentioning

confidence: 99%

Hiding Image within Image Based on Deep Learning

Yang¹,

Zhang²,

Wu³

et al. 2022

J. Phys.: Conf. Ser.

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Information hiding technology is a technology that transmits secret information through a carrier, and is a research hotspot in the field of information security. The traditional information hiding algorithm relies on the meticulous design of human beings, and obtains the dense image through modification. With the development of deep learning, the integration of information hiding technology and deep learning has resulted in many information hiding technologies based on deep learning. Among them, image hiding has become a research hotspot due to its large steganographic capacity. Therefore, this paper reviews the information hiding technology based on deep learning. According to the difference of hidden models, it is analysed from four aspects: (1) information hiding model based on encoder-decoder; (2) information hiding model based on generative adversarial network; (3) information hiding model based on invertible network; (4) information hiding model based on neural network information hiding models for style transfer. Finally, these models are analysed and compared, and the future development direction is discussed and prospected.

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Section: Information Hiding Model Based On Encoder-decoder Networkmentioning

confidence: 99%

Hiding Image within Image Based on Deep Learning

Yang¹,

Zhang²,

Wu³

et al. 2022

J. Phys.: Conf. Ser.

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show abstract

“…From the residual image, we can clearly find some information from the secret image. In order to solve this problem, Duan et al [12] and Sharma et al [11] proposed to encrypt the secret image first and then embed the encrypted secret image into the cover image. Although these methods can hide the specific content of the secret image, the attacker may still think that the stego image is modified from cover image.…”

Section: High-capacity Image Steganography Algorithmsmentioning

confidence: 99%

“…Part of the semantic information of the secret image will be discovered. In response to this problem, some scholars have proposed that the secret image can be encrypted before embedding [11,12], although this method can solve the problem of the semantic information exposure of the secret image. However, if an attacker can obtain cover and stego images, he may think that the stego image is the result of cover image modification and will still question the existence of secret communication.…”

Section: Introductionmentioning

confidence: 99%

High-Capacity Image Steganography Algorithm Based on Image Style Transfer

Yang

Wang

et al. 2021

Security and Communication Networks

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Steganography is a technique for publicly transmitting secret information through a cover. Most of the existing steganography algorithms are based on modifying the cover image, generating a stego image that is very similar to the cover image but has different pixel values, or establishing a mapping relationship between the stego image and the secret message. Attackers will discover the existence of secret communications from these modifications or differences. In order to solve this problem, we propose a steganography algorithm ISTNet based on image style transfer, which can convert a cover image into another stego image with a completely different style. We have improved the decoder so that the secret image features can be fused with style features in a variety of sizes to improve the accuracy of secret image extraction. The algorithm has the functions of image steganography and image style transfer at the same time, and the images it generates are both stego images and stylized images. Attackers will pay more attention to the style transfer side of the algorithm, but it is difficult to find the steganography side. Experiments show that our algorithm effectively increases the steganography capacity from 0.06 bpp to 8 bpp, and the generated stylized images are not significantly different from the stylized images on the Internet.

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“…Every smart device is "monitoring" you, so personal data protection and privacy-preserved problems should be paid more attention to. Especially the release of GDPR in EU and EEA in 2016, more and more researchers have been digging into privacy-related works [12][13][14][15][16][17][18][19].…”

Section: Introductionmentioning

confidence: 99%

A Real‐Time and Long‐Term Face Tracking Method Using Convolutional Neural Network and Optical Flow in IoT‐Based Multimedia Communication Systems

Ren

Myint

et al. 2021

Wireless Communications and Mobile Computing

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The development of the Internet of Things (IoT) stimulates many research works related to Multimedia Communication Systems (MCS), such as human face detection and tracking. This trend drives numerous progressive methods. Among these methods, the deep learning-based methods can spot face patch in an image effectively and accurately. Many people consider face tracking as face detection, but they are two different techniques. Face detection focuses on a single image, whose shortcoming is obvious, such as unstable and unsmooth face position when adopted on a sequence of continuous images; computing is expensive due to its heavy reliance on Convolutional Neural Networks (CNNs) and limited detection performance on the edge device. To overcome these defects, this paper proposes a novel face tracking strategy by combining CNN and optical flow, namely, C-OF, which achieves an extremely fast, stable, and long-term face tracking system. Two key things for commercial applications are the stability and smoothness of face positions in a sequence of image frames, which can provide more probability for face biological signal extraction, silent face antispoofing, and facial expression analysis in the fields of IoT-based MCS. Our method captures face patterns in every two consequent frames via optical flow to get rid of the unstable and unsmooth problems. Moreover, an innovative metric for measuring the stability and smoothness of face motion is designed and adopted in our experiments. The experimental results illustrate that our proposed C-OF outperforms both face detection and object tracking methods.

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Hiding Data in Images Using Cryptography and Deep Neural Network

Cited by 126 publications

References 35 publications

Hiding Image within Image Based on Deep Learning

Hiding Image within Image Based on Deep Learning

High-Capacity Image Steganography Algorithm Based on Image Style Transfer

A Real‐Time and Long‐Term Face Tracking Method Using Convolutional Neural Network and Optical Flow in IoT‐Based Multimedia Communication Systems

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