Cryptanalysis and Enhancement of an Image Encryption Scheme Based on Bit-Plane Extraction and Multiple Chaotic Maps

Liu, Yu; Wu, Jiahui

doi:10.1109/access.2019.2916600

Cited by 18 publications

(7 citation statements)

References 41 publications

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“…The network realizes image contrast enhancement by learning some mapping relationships between the input images and the noise maps. Considering the weak representation ability of a shallow network, it is necessary to increase the network depth [38]- [43]. However, as the network depth increases, the difficulty of network training will increase, as well.…”

Section: A Establishment Of the Neural Networkmentioning

confidence: 99%

An Improved Enhancement Algorithm Based on CNN Applicable for Weak Contrast Images

Wang

2020

IEEE Access

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Image enhancement is commonly used in digital image processing applications. Through image enhancement, the features of the target object are enhanced to make it easier to identify. In order to realize image enhancement, a neural network with a trapezoidal convolution kernel is proposed in this paper. First, weak contrast images are generated using the images in SIDD, DND and RENOIR, which are public datasets used for image denoising. On this basis, pixel distribution characteristics of the R, G and B channels of the weak contrast images are analyzed, and the histogram distribution of the images is given, which can be used to determine the size of the network convolution kernels. Then, a multi-layer convolution neural network is constructed, which uses the original image as an input and outputs the noise map. This network includes convolution kernels of different sizes for the three channels of the original images, and the convolution kernel size is determined by calculating the mean square deviation of the pixels of the corresponding channel. The proposed algorithm provides visually pleasing contrast enhancement. In this paper, a public dataset for image enhancement and actual captured images are separately assessed, both the contrast experiment and ablation experiment results show that the algorithm proposed in this paper can achieve a higher Peak Signal to Noise Ratio (PSNR) and a higher Structural Similarity Index (SSIM) than other image enhancement algorithms. INDEX TERMS Enhancement algorithm, trapezoidal convolution Kernel, image contrast, feature analysis, neural network.

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Section: A Establishment Of the Neural Networkmentioning

confidence: 99%

An Improved Enhancement Algorithm Based on CNN Applicable for Weak Contrast Images

Wang

2020

IEEE Access

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“…In the same way, Li et al [ 19 ] broke Pak’s work [ 12 ] by comparing the differences between several pairs of chosen plain-cipher images. After analyzing the image cryptosystem [ 9 ], Liu et al [ 20 ] reported that the correlation information among adjacent plain pixels cannot be erased by the row/column-wise permutation, thereby leaving an open door for the known-plaintext attack. Wen and Yu [ 21 ] ascertained that, in the image cryptosystem [ 9 ], keys are not associated with the plain images.…”

Section: Introductionmentioning

confidence: 99%

On the Cryptanalysis of a Latin Cubes-Based Image Cryptosystem

Liu

Liao

Dong

2021

Entropy

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Based on orthogonal Latin cubes, an image cryptosystem with confusion–diffusion–confusion cipher architecture has been proposed recently (Inf. Sci.2019, 478, 1–14). However, we find that there are four fatal vulnerabilities in this image cryptosystem, which leave open doors for cryptanalysis. In this paper, we propose a reference-validation inference algorithm and design screening-based rules to efficiently break the image cryptosystem. Compared with an existing cryptanalysis algorithm, the proposed method requires fewer pairs of chosen plain-cipher images, and behaves stably since different keys, positions of chosen bits and contents of plain images will not affect the cryptanalysis performance. Experimental results show that our cryptanalysis algorithm only requirespairs of chosen plain-cipher images, whererepresents the image’s resolution. Comparative studies demonstrate effectiveness and superiority of the proposed cryptanalysis algorithm.

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“…Up to now, a large number of image cryptosystems based on chaotic systems have been found to be unsafe [24,25,26,27,28,29,30,31]. These cryptosystems cannot resist chosen/known plaintext attacks [32,33,34,35,36,37,38,39,40,41] because they possess plaintext-unrelated equivalent keys. A chosen plaintext attack method in [32] cracked the scheme in [24] with only 18 attacks for 256×256-sized images.…”

Section: Introductionmentioning

confidence: 99%

“…They also presented a chosen plaintext attack method to decode the scheme in [25] with MN+1 pairs of chosen plaintext and cipher-text images, where M and N are the height and width of the image, respectively. In [34], Liu et al proposed an effective known plaintext attack method and an effective chosen plaintext attack method to crack the scheme in [26], and suggested some improvement measures. Wen et al in [36] successfully cracked the image encryption scheme in [27] with the help of a chosen plaintext attack and a chosen cipher-text attack.…”

Section: Introductionmentioning

confidence: 99%

“…From the above, it can be seen that these image cryptosystems [24,25,26,27,28,29,30,31] are insecure, not because they used the chaotic systems with simple dynamic equations, nor because the statistical characteristics of chaotic pseudo-random sequences used are not good, but because their confusion or diffusion schemes are not well designed, which makes their equivalent keys directly involved in image encryption insensitive to plain images. These image cryptosystems are separately cracked by [32,33,34,35,36,37,38,39,40,41], and the main decoding algorithms are the chosen/known plaintext methods. The objects of attack mainly focus on the equivalent keys [42].…”

Section: Introductionmentioning

confidence: 99%

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The Memorable Image Encryption Algorithm Based on Neuron-Like Scheme

Zhang

2020

IEEE Access

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A new image cryptosystem based on neuron-like scheme is proposed in this paper. As a nonlinear component of image cryptosystem, neuron-like learning algorithm can realize a kind of memorable diffusion algorithm, thus realizing the plaintext-related image encryption algorithm. Through the feedback operation, the inputs and weights of the neuron are regulated by the information of the image, and then they are used to diffuse the image information. According to the needs of application scenarios, the number of inputs of the neuron can be adjusted so that the image cryptosystem can be applied to different security requirements. The simulation results show that the proposed image encryption system is extremely sensitive to the secret keys, the equivalent keys and the plain images, and also possesses the high security and adaptive characteristics. The image cryptosystem can be applied to the actual applications of image information security.

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Cryptanalysis and Enhancement of an Image Encryption Scheme Based on Bit-Plane Extraction and Multiple Chaotic Maps

Cited by 18 publications

References 41 publications

An Improved Enhancement Algorithm Based on CNN Applicable for Weak Contrast Images

An Improved Enhancement Algorithm Based on CNN Applicable for Weak Contrast Images

On the Cryptanalysis of a Latin Cubes-Based Image Cryptosystem

The Memorable Image Encryption Algorithm Based on Neuron-Like Scheme

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