Recompression of JPEG Crypto-Compressed Images Without a Key

Itier, Vincent; Puteaux, Pauline; Puech, William

doi:10.1109/tcsvt.2019.2894520

Cited by 35 publications

(12 citation statements)

References 39 publications

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“…Compressible encryption methods have been also proposed that consider both security and efficient compression so that they can be adapted to cloud storage and network sharing [8]- [16]. Some of them [11]- [15] can be applied to traditional ML algorithms, such as support vector machine (SVM), k-nearest neighbors (KNN), and random forest, even under the use of the kernel trick [17].…”

Section: Related Work a Visual Information Protectionmentioning

confidence: 99%

Pixel-Based Image Encryption Without Key Management for Privacy-Preserving Deep Neural Networks

2019

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We present a novel privacy-preserving scheme for deep neural networks (DNNs) that enables us not to only apply images without visual information to DNNs but to also consider the use of independent encryption keys for both training and testing images for the first time. In this paper, a novel pixel-based image encryption method that maintains important features of original images is proposed for privacy-preserving DNNs. For training, a DNN model is trained with images encrypted by using the proposed method with independent encryption keys. For testing, the model enables us to apply both encrypted images and plain images for image classification. Therefore, there is no need to manage keys. In addition, the proposed method allows us to perform data augmentation in the encrypted domain. In an experiment, the proposed method is applied to well-known networks, that is, deep residual networks and densely connected convolutional networks, for image classification. The experimental results demonstrate that the proposed method, under the use of independent encryption keys, can maintain a high classification performance, and it is robust against ciphertext-only attacks (COAs). Moreover, the results confirm that the proposed scheme is able to classify plain images as well as encrypted images, even when data augmentation is carried out in the encrypted domain. INDEX TERMS Deep learning, deep neural network, image encryption, privacy-preserving.

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Section: Related Work a Visual Information Protectionmentioning

confidence: 99%

Pixel-Based Image Encryption Without Key Management for Privacy-Preserving Deep Neural Networks

2019

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“…Compressible encryption methods have been also proposed that consider both security and efficient compression so that they can be adapted to cloud storage and photo sharing services [9]- [14]. Some of them [11]- [13] can be applied to traditional machine learning algorithms, such as support vector machine, k-nearest neighbors, and random forest, even under the use of the kernel trick [15], [16].…”

Section: A Protecting Visual Informationmentioning

confidence: 99%

Image to Perturbation: An Image Transformation Network for Generating Visually Protected Images for Privacy-Preserving Deep Neural Networks

et al. 2021

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We propose a novel image transformation network for generating visually protected images for privacy-preserving deep neural networks (DNNs). The proposed transformation network is trained by using a plain image dataset so that plain images are converted into visually protected ones. Conventional perceptual encryption methods cause some accuracy degradation in image classification and are not robust enough against state-of-the-art attacks. In contrast, the proposed network not only enables us to maintain the image classification accuracy that using plain images achieves but is also strongly robust against attacks including DNN-based ones. Furthermore, there is no need to manage any security keys as the conventional methods require. In an image classification experiment, the proposed network is demonstrated to strongly protect the visual information of plain images while maintaining a high classification accuracy under the use of two typical classification networks: ResNet and VGG. In addition, it is shown that the visually protected images are robust enough against various attacks in an experiment in which we tried to restore the visual information of plain images.

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“…However, cloud providers are not trusted in general, so there is the possibility of data leakage and unauthorized use in cloud environments. Therefore, various privacy-preserving image identification, retrieval, and processing schemes have been studied for untrusted cloud environments [3]- [21].…”

Section: Introductionmentioning

confidence: 99%

Privacy-Preserving Content-Based Image Retrieval Using Compressible Encrypted Images

Iida

Kiya

2020

IEEE Access

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In this paper, we propose a novel content basedimage retrieval scheme allowing the mixed use of encrypted and plain images for the first time. In the proposed scheme, images are encrypted by a block-scrambling method developed for encryption-then-compression (EtC) systems. The encrypted images, referred to as EtC images, can be compressed with JPEG, as well as for plain images. Image descriptors used for the proposed retrieval is designed to avoid the effect of image encryption. As a result, the use of EtC images and the descriptors allows us to carry out retrieval of both encrypted images and plain ones. In an experiment, the proposed scheme is demonstrated to have the same performance as conventional retrieval methods with plain images, even under the mixed use of plain images and EtC ones.

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Recompression of JPEG Crypto-Compressed Images Without a Key

Cited by 35 publications

References 39 publications

Pixel-Based Image Encryption Without Key Management for Privacy-Preserving Deep Neural Networks

Pixel-Based Image Encryption Without Key Management for Privacy-Preserving Deep Neural Networks

Image to Perturbation: An Image Transformation Network for Generating Visually Protected Images for Privacy-Preserving Deep Neural Networks

Privacy-Preserving Content-Based Image Retrieval Using Compressible Encrypted Images

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