Privacy-Preserving Image Classification With Deep Learning and Double Random Phase Encoding

Yi, Faliu; Jeong, Ongee; Moon, Inkyu

doi:10.1109/access.2021.3116876

Cited by 9 publications

(6 citation statements)

References 44 publications

(77 reference statements)

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“…In the study of [Sheidani et al, 2021] have used reversible data hiding scheme along with using elliptical curve encryption towards resisting chosen plaintext attack. Adoption of deep learning is also reported in work of [Yi et al, 2021] where encoding and deep learning scheme has been implemented in order to facilitate image classification. The model discussed by [Jiang et al, 2021] have used encrypted domain over image feature in order to secure the privacy information within the image.…”

Section: Literature Reviewmentioning

confidence: 99%

Analytical and Integrated Framework for Privacy Presrvation of Images in Cloud Ecosystem

Dule¹,

R²

2023

INDJCSE

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Privacy preservation is always a potential concern while storing or transmitting images with sensitive information within them. A review of existing privacy preservation techniques shows the potential usage of deep learning, adoption of highly sophisticated encryption, lack of packetization scheme, and focus on a singular attack. Therefore, the proposed study presents a novel analytical framework for the privacy preservation of image data in the cloud environment. A novel and simplified mathematical model with layer-based operation is constructed, which facilitates appending image chunks with digital security code and encryption token using a hash to generate a hash vector of an image with a higher degree of secrecy. An extensive analysis is carried out over different datasets of images to find that the proposed scheme offers consistency in its performance and a significant balance between security and computational performance in contrast to frequently exercised privacy preservation schemes. The study outcome exhibited 59% overhead reduction, 25% enhanced signal quality, 65% better resolution, and 40% faster execution time in contrast to the existing scheme.

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Section: Literature Reviewmentioning

confidence: 99%

Analytical and Integrated Framework for Privacy Presrvation of Images in Cloud Ecosystem

Dule¹,

R²

2023

INDJCSE

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“…The main advantage of their method is that it does not require an adaptation network to achieve classification accuracy on the encrypted images comparable to that of the plain images. On the other hand, learnable PE methods have been proposed to enable PPDL applications in [ 5 , 6 , 7 , 8 , 26 , 27 , 28 ]. In [ 5 ], they have proposed a PE method that forms a 6-channel image from an RGB image by splitting predefined blocks into upper and lower 4-bit images.…”

Section: Related Workmentioning

confidence: 99%

“…Alternatively, [ 26 ] proposed to divide the encrypted image of [ 6 ] into blocks and apply three different types of filters on randomly selected blocks. Furthermore, the aforementioned methods that perform digital encryption, [ 27 , 28 ], have proposed optical image encryption methods to take advantage of parallel computing for PPDL.…”

Section: Related Workmentioning

confidence: 99%

IIB–CPE: Inter and Intra Block Processing-Based Compressible Perceptual Encryption Method for Privacy-Preserving Deep Learning

Ahmad

Shin

2022

Sensors

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Perceptual encryption (PE) of images protects visual information while retaining the intrinsic properties necessary to enable computation in the encryption domain. Block–based PE produces JPEG-compliant images with almost the same compression savings as that of the plain images. The methods represent an input color image as a pseudo grayscale image to benefit from a smaller block size. However, such representation degrades image quality and compression savings, and removes color information, which limits their applications. To solve these limitations, we proposed inter and intra block processing for compressible PE methods (IIB–CPE). The method represents an input as a color image and performs block-level inter processing and sub-block-level intra processing on it. The intra block processing results in an inside–out geometric transformation that disrupts the symmetry of an entire block thus achieves visual encryption of local details while preserving the global contents of an image. The intra block-level processing allows the use of a smaller block size, which improves encryption efficiency without compromising compression performance. Our analyses showed that IIB–CPE offers 15% bitrate savings with better image quality than the existing PE methods. In addition, we extended the scope of applications of the proposed IIB–CPE to the privacy-preserving deep learning (PPDL) domain.

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“…Another approach for protecting copyright and privacy information in test images is to conceal the visual information. Image encryption is a typical technique for concealing visual information, and image-encryption methods have been actively studied to train encrypted images using deep neural networks [ 3 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 ]. The method in [ 3 ] combines federated learning with image encryption for test images.…”

Section: Introductionmentioning

confidence: 99%

“…Encrypted image classification via a cloud server assumes that a user encrypts test images and transmits the encrypted images to a server. Thus, it is desirable to be able to compress the encrypted images in terms of the transmission efficiency; however, most such methods [ 3 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 ] do not consider image compression. Aprilpyone et al employed the encryption-then-compression (EtC) system [ 18 ] as an image encryption algorithm so that the encrypted images (hereafter, EtC images) possess a high compression performance [ 8 ].…”

Section: Introductionmentioning

confidence: 99%

Effects of JPEG Compression on Vision Transformer Image Classification for Encryption-then-Compression Images

Hamano

Imaizumi

Kiya

2023

Sensors

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This paper evaluates the effects of JPEG compression on image classification using the Vision Transformer (ViT). In recent years, many studies have been carried out to classify images in the encrypted domain for privacy preservation. Previously, the authors proposed an image classification method that encrypts both a trained ViT model and test images. Here, an encryption-then-compression system was employed to encrypt the test images, and the ViT model was preliminarily trained by plain images. The classification accuracy in the previous method was exactly equal to that without any encryption for the trained ViT model and test images. However, even though the encrypted test images can be compressible, the practical effects of JPEG, which is a typical lossy compression method, have not been investigated so far. In this paper, we extend our previous method by compressing the encrypted test images with JPEG and verify the classification accuracy for the compressed encrypted-images. Through our experiments, we confirm that the amount of data in the encrypted images can be significantly reduced by JPEG compression, while the classification accuracy of the compressed encrypted-images is highly preserved. For example, when the quality factor is set to 85, this paper shows that the classification accuracy can be maintained at over 98% with a more than 90% reduction in the amount of image data. Additionally, the effectiveness of JPEG compression is demonstrated through comparison with linear quantization. To the best of our knowledge, this is the first study to classify JPEG-compressed encrypted images without sacrificing high accuracy. Through our study, we have come to the conclusion that we can classify compressed encrypted-images without degradation to accuracy.

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Privacy-Preserving Image Classification With Deep Learning and Double Random Phase Encoding

Cited by 9 publications

References 44 publications

Analytical and Integrated Framework for Privacy Presrvation of Images in Cloud Ecosystem

Analytical and Integrated Framework for Privacy Presrvation of Images in Cloud Ecosystem

IIB–CPE: Inter and Intra Block Processing-Based Compressible Perceptual Encryption Method for Privacy-Preserving Deep Learning

Effects of JPEG Compression on Vision Transformer Image Classification for Encryption-then-Compression Images

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