Privacy-Preserving Deep Neural Network Methods: Computational and Perceptual Methods—An Overview

Saj, Raghida El; Gooya, Ehsan Sedgh; Alfalou, Ayman; Khalil, Mohamad

doi:10.3390/electronics10111367

Cited by 13 publications

(7 citation statements)

References 21 publications

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“…Some of these techniques implement ML techniques such as artificial neural network (ANN). With the help of pooling layers, the hidden features can be derived [ 10 ]. Generally, the output of the final pooling layer was implemented for the purposes of regression and classification.…”

Section: Introductionmentioning

confidence: 99%

Privacy Preserving Image Encryption with Optimal Deep Transfer Learning Based Accident Severity Classification Model

Sirisha

Chandana

2023

Sensors

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Effective accident management acts as a vital part of emergency and traffic control systems. In such systems, accident data can be collected from different sources (unmanned aerial vehicles, surveillance cameras, on-site people, etc.) and images are considered a major source. Accident site photos and measurements are the most important evidence. Attackers will steal data and breach personal privacy, causing untold costs. The massive number of images commonly employed poses a significant challenge to privacy preservation, and image encryption can be used to accomplish cloud storage and secure image transmission. Automated severity estimation using deep-learning (DL) models becomes essential for effective accident management. Therefore, this article presents a novel Privacy Preserving Image Encryption with Optimal Deep-Learning-based Accident Severity Classification (PPIE-ODLASC) method. The primary objective of the PPIE-ODLASC algorithm is to securely transmit the accident images and classify accident severity into different levels. In the presented PPIE-ODLASC technique, two major processes are involved, namely encryption and severity classification (i.e., high, medium, low, and normal). For accident image encryption, the multi-key homomorphic encryption (MKHE) technique with lion swarm optimization (LSO)-based optimal key generation procedure is involved. In addition, the PPIE-ODLASC approach involves YOLO-v5 object detector to identify the region of interest (ROI) in the accident images. Moreover, the accident severity classification module encompasses Xception feature extractor, bidirectional gated recurrent unit (BiGRU) classification, and Bayesian optimization (BO)-based hyperparameter tuning. The experimental validation of the proposed PPIE-ODLASC algorithm is tested utilizing accident images and the outcomes are examined in terms of many measures. The comparative examination revealed that the PPIE-ODLASC technique showed an enhanced performance of 57.68 dB over other existing models.

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Section: Introductionmentioning

confidence: 99%

Privacy Preserving Image Encryption with Optimal Deep Transfer Learning Based Accident Severity Classification Model

Sirisha

Chandana

2023

Sensors

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“…To the best of our knowledge, Refs. [5,40,41] are the most related surveys to the current study that deal with the JPEG-compatible perceptual encryption schemes. In [40,41], the authors studied CPE and noncompressible perceptual encryption methods mainly from a PPML application point of view.…”

Section: Introductionmentioning

confidence: 99%

“…[5,40,41] are the most related surveys to the current study that deal with the JPEG-compatible perceptual encryption schemes. In [40,41], the authors studied CPE and noncompressible perceptual encryption methods mainly from a PPML application point of view. On the other hand, the authors in [5] focused on joint compression and encryption algorithms in general and covered only two CPE schemes.…”

Section: Introductionmentioning

confidence: 99%

Comprehensive Analysis of Compressible Perceptual Encryption Methods—Compression and Encryption Perspectives

Ahmad

Choi

Shin

2023

Sensors

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Perceptual encryption (PE) hides the identifiable information of an image in such a way that its intrinsic characteristics remain intact. This recognizable perceptual quality can be used to enable computation in the encryption domain. A class of PE algorithms based on block-level processing has recently gained popularity for their ability to generate JPEG-compressible cipher images. A tradeoff in these methods, however, is between the security efficiency and compression savings due to the chosen block size. Several methods (such as the processing of each color component independently, image representation, and sub-block-level processing) have been proposed to effectively manage this tradeoff. The current study adapts these assorted practices into a uniform framework to provide a fair comparison of their results. Specifically, their compression quality is investigated under various design parameters, such as the choice of colorspace, image representation, chroma subsampling, quantization tables, and block size. Our analyses have shown that at best the PE methods introduce a decrease of 6% and 3% in the JPEG compression performance with and without chroma subsampling, respectively. Additionally, their encryption quality is quantified in terms of several statistical analyses. The simulation results show that block-based PE methods exhibit several favorable properties for the encryption-then-compression schemes. Nonetheless, to avoid any pitfalls, their principal design should be carefully considered in the context of the applications for which we outlined possible future research directions.

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“…It has become a trendy and interesting topic. 1,2 Although it faces many challenges, especially those related to data availability and privacy, 3,4 machine learning has been able to develop and prove itself in many fields and applications, such as image processing, computer vision [5][6][7][8] ... Several types of machine learning algorithms were used for data classification; different neural networks architecture were applied, [9][10][11] new models and approaches are still developed. A new machine learning approach for data classification based on generative adversarial networks (GANs) has been presented in this paper, where the classification problem has been converted to identity identification problem.…”

Section: Introductionmentioning

confidence: 99%

Generative classifier pix2pix

Saj

Gooya²,

Alfalou³

et al. 2023

Pattern Recognition and Tracking XXXIV

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Generative adversarial networks have been widely developed to generate new data, and they have been used for several different applications. Some networks have been developed to classify data at the discriminator level, either by modifying the loss function or by adding a classifier. In this paper, the generative classifier pix2pix, a classifier based on generative adversarial networks, specifically the pix2pix, has been introduced. The classification is done without the need to keep the discriminator or to add additional networks, only the generator is used to classify the data. This classification requires the preparation of a reference dataset. The generative classifier pix2pix was applied to the GC character recognition task using 50000 images for training, it achieved 99.36%. It was also applied to the ORL face recognition task using 360 images for training, and it achieved an average of 97.99%.

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Privacy-Preserving Deep Neural Network Methods: Computational and Perceptual Methods—An Overview

Cited by 13 publications

References 21 publications

Privacy Preserving Image Encryption with Optimal Deep Transfer Learning Based Accident Severity Classification Model

Privacy Preserving Image Encryption with Optimal Deep Transfer Learning Based Accident Severity Classification Model

Comprehensive Analysis of Compressible Perceptual Encryption Methods—Compression and Encryption Perspectives

Generative classifier pix2pix

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