Warit Sirichotedumrong

Warit Sirichotedumrong, Hitoshi Kiya

Various visual information protection methods have been proposed for privacy-preserving deep neural networks (DNNs). In contrast, attack methods on such protection methods have been studied simultaneously. In this paper, we evaluate state-of-the-art visual protection methods for privacy-preserving DNNs in terms of visual security against ciphertext-only attacks (COAs). We focus on brute-force attack, feature reconstruction attack (FR-Attack), inverse transformation attack (ITN-Attack), and GAN-based attack (GAN-Attack), which have been proposed to reconstruct visual information on plain images from the visually-protected images. The detail of various attack is first summarized, and then visual security of the protection methods is evaluated. Experimental results demonstrate that most of protection methods, including pixel-wise encryption, have not enough robustness against GAN-Attack, while a few protection methods are robust enough against GAN-Attack.

Warit Sirichotedumrong, Hitoshi Kiya

We propose a novel image transformation scheme using generative adversarial networks (GANs) for privacy-preserving deep neural networks (DNNs). The proposed scheme enables us not only to apply images without visual information to DNNs, but also to enhance robustness against ciphertext-only attacks (COAs) including DNN-based attacks. In this paper, the proposed transformation scheme is demonstrated to be able to protect visual information on plain images, and the visually-protected images are directly applied to DNNs for privacy-preserving image classification. Since the proposed scheme utilizes GANs, there is no need to manage encryption keys. In an image classification experiment, we evaluate the effectiveness of the proposed scheme in terms of classification accuracy and robustness against COAs.

Warit Sirichotedumrong, Yuma Kinoshita, Hitoshi Kiya

This paper aims to evaluate the safety of a pixel-based image encryption method, which has been proposed to apply images with no visual information to deep neural networks (DNN), in terms of robustness against ciphertext-only attacks (COA). In addition, we propose a novel DNN-based COA that aims to reconstruct the visual information of encrypted images. The effectiveness of the proposed attack is evaluated under two encryption key conditions: same encryption key, and different encryption keys. The results show that the proposed attack can recover the visual information of the encrypted images if images are encrypted under same encryption key. Otherwise, the pixel-based image encryption method has robustness against COA.

MaungMaung AprilPyone, Warit Sirichotedumrong, Hitoshi Kiya

Data for deep learning should be protected for privacy preserving. Researchers have come up with the notion of learnable image encryption to satisfy the requirement. However, existing privacy preserving approaches have never considered the threat of adversarial attacks. In this paper, we ran an adversarial test on learnable image encryption in five different scenarios. The results show different behaviors of the network in the variable key scenarios and suggest learnable image encryption provides certain level of adversarial robustness.

Warit Sirichotedumrong, Takahiro Maekawa, Yuma Kinoshita et al.

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 for both training and testing but to also consider data augmentation in the encrypted domain for the first time. In this paper, a novel pixel-based image encryption method is first proposed for privacy-preserving DNNs. In addition, a novel adaptation network is considered that reduces the influence of image encryption. In an experiment, the proposed method is applied to a well-known network, ResNet-18, for image classification. The experimental results demonstrate that conventional privacy-preserving machine learning methods including the state-of-the-arts cannot be applied to data augmentation in the encrypted domain and that the proposed method outperforms them in terms of classification accuracy.

Warit Sirichotedumrong, Tatsuya Chuman, Hitoshi Kiya

A new grayscale-based block scrambling image encryption scheme is presented to enhance the security of Encryption-then-Compression (EtC) systems, which are used to securely transmit images through an untrusted channel provider. The proposed scheme enables the use of a smaller block size and a larger number of blocks than the conventional scheme. Images encrypted using the proposed scheme include less color information due to the use of grayscale images even when the original image has three color channels. These features enhance security against various attacks, such as jigsaw puzzle solver and brute-force attacks. Moreover, it allows the use of color sub-sampling, which can improve the compression performance, although the encrypted images have no color information. In an experiment, encrypted images were uploaded to and then downloaded from Facebook and Twitter, and the results demonstrated that the proposed scheme is effective for EtC systems, while maintaining a high compression performance.

Tatsuya Chuman, Warit Sirichotedumrong, Hitoshi Kiya

A block scrambling-based encryption scheme is presented to enhance the security of Encryption-then-Compression (EtC) systems with JPEG compression, which allow us to securely transmit images through an untrusted channel provider, such as social network service providers. The proposed scheme enables the use of a smaller block size and a larger number of blocks than the conventional scheme. Images encrypted using the proposed scheme include less color information due to the use of grayscale images even when the original image has three color channels. These features enhance security against various attacks such as jigsaw puzzle solver and brute-force attacks. In an experiment, the security against jigsaw puzzle solver attacks is evaluated. Encrypted images were uploaded to and then downloaded from Facebook and Twitter, and the results demonstrated that the proposed scheme is effective for EtC systems.

Warit Sirichotedumrong, Tatsuya Chuman, Hitoshi Kiya

This paper considers a new grayscale-based image encryption for Encryption-then-Compression (EtC) systems with JPEG compression. Firstly, generation methods of grayscale-based images are discussed in terms of the selection of color space. In addition, a new JPEG quantization table for the grayscale-based images is proposed to provide a better compression performance. Moreover, the quality of both images uploaded to Social Network Services (SNS) and downloaded from SNS, are discussed and evaluated. In the experiments, encrypted images are compressed using various compression parameters and quantization tables, and uploaded to Twitter and Facebook. The results proved that the selection of color space and the proposed quantization table can improve the compression performances of not only uploaded images but also downloaded ones.

Tatsuya Chuman, Kenta Iida, Warit Sirichotedumrong et al.

Encryption-then-Compression (EtC) systems have been proposed to securely transmit images through an untrusted channel provider. In this study, EtC systems were applied to social media like Twitter that carry out image manipulations. The block scrambling-based encryption schemes used in EtC systems were evaluated in terms of their robustness against image manipulation on social media. The aim was to investigate how five social networking service (SNS) providers, Facebook, Twitter, Google+, Tumblr and Flickr, manipulate images and to determine whether the encrypted images uploaded to SNS providers can avoid being distorted by such manipulations. In an experiment, encrypted and non-encrypted JPEG images were uploaded to various SNS providers. The results show that EtC systems are applicable to the five SNS providers.

Warit Sirichotedumrong, Tatsuya Chuman, Shoko Imaizumi et al.

This paper proposes a new block scrambling encryption scheme that enhances the security of encryption-then-compression (EtC) systems for JPEG images, which are used, for example, to securely transmit images through an untrusted channel provider. The proposed method allows the use of a smaller block size and a larger number of blocks than the conventional ones. Moreover, images encrypted using proposed scheme include less color information due to the use of grayscale even when the original image has three color channels. These features enhance security against various attacks such as jigsaw puzzle solver and brute-force attacks. The results of an experiment in which encrypted images were uploaded to and then downloaded from Twitter and Facebook demonstrated the effectiveness of the proposed scheme for EtC systems.