Recoverable Privacy-Preserving Image Classification through Noise-like Adversarial Examples

With the increasing prevalence of cloud computing platforms, ensuring data privacy during the cloud-based image related services such as classification has become crucial. In this study, we propose a novel privacypreserving image classification scheme that enables the direct application of classifiers trained in the plaintext domain to classify encrypted images, without the need of retraining a dedicated classifier. Moreover, encrypted images can be decrypted back into their original form with high fidelity (recoverable) using a secret key. Specifically, our proposed scheme involves utilizing a feature extractor and an encoder to mask the plaintext image through a newly designed Noise-like Adversarial Example (NAE). Such an NAE not only introduces a noise-like visual appearance to the encrypted image but also compels the target classifier to predict the ciphertext as the same label as the original plaintext image. At the decoding phase, we adopt a Symmetric Residual Learning (SRL) framework for restoring the plaintext image with minimal degradation. Extensive experiments demonstrate that 1) the classification accuracy of the classifier trained in the plaintext domain remains the same in both the ciphertext and plaintext domains; 2) the encrypted images can be recovered into their original form with an average PSNR of up to 51+ dB for the SVHN dataset and 48+ dB for the VGGFace2 dataset; 3) our system exhibits satisfactory generalization capability on the encryption, decryption and classification tasks across datasets that are different from the training one; and 4) a high-level of security is achieved against three potential threat models. The code is available at https://github.com/csjunjun/RIC.git.Comment: 23 pages, 9 figure

DifAttack: Query-Efficient Black-Box Attack via Disentangled Feature Space

This work investigates efficient score-based black-box adversarial attacks with a high Attack Success Rate (ASR) and good generalizability. We design a novel attack method based on a Disentangled Feature space, called DifAttack, which differs significantly from the existing ones operating over the entire feature space. Specifically, DifAttack firstly disentangles an image's latent feature into an adversarial feature and a visual feature, where the former dominates the adversarial capability of an image, while the latter largely determines its visual appearance. We train an autoencoder for the disentanglement by using pairs of clean images and their Adversarial Examples (AEs) generated from available surrogate models via white-box attack methods. Eventually, DifAttack iteratively optimizes the adversarial feature according to the query feedback from the victim model until a successful AE is generated, while keeping the visual feature unaltered. In addition, due to the avoidance of using surrogate models' gradient information when optimizing AEs for black-box models, our proposed DifAttack inherently possesses better attack capability in the open-set scenario, where the training dataset of the victim model is unknown. Extensive experimental results demonstrate that our method achieves significant improvements in ASR and query efficiency simultaneously, especially in the targeted attack and open-set scenarios. The code will be available at https://github.com/csjunjun/DifAttack.git soon

Effects of Personality on Trading Performance in Social Trading Platforms

Social trading platforms offer opportunities for amateur investors to copy professional traders’ behavior. However, past studies on behavioral finance have largely neglected the role of personality in shaping traders’ behavior. To this end, we aim to scrutinize the effects of leader traders’ personality on their trading behaviors and subsequent performance on social trading platforms. Particularly, we employ the Myers–Briggs Type Indicator (MBTI) personality classification scheme to delineate leader traders’ personality into the four dimensions of Extraversion-Introversion (E-I), Sensing-Intuition (S-N), Thinking-Feeling (T-F), and Judging-Perceiving (J-P). Next, we draw on machine learning techniques to advance a novel text-based approach for extracting the personality dimensions of leader traders automatically. Analytical results attest to the impact of personality dimensions on trading behavior and that of trading behavior on performance. Findings from this study yield insights for both social trading platforms and followers by identifying profitable leader traders based on their personality

Metabolic engineering of \u3ci\u3eEscherichia coli\u3c/i\u3e for the \u3ci\u3ede novo\u3c/i\u3e stereospecific biosynthesis of 1,2-propanediol through lactic acid

1,2-propanediol (1,2-PDO) is an industrial chemical with a broad range of applications, such as the production of alkyd and unsaturated polyester resins. It is currently produced as a racemic mixture from nonrenewable petroleum-based feedstocks. We have reported a novel artificial pathway for the biosynthesis of 1,2-PDO via lactic acid isomers as the intermediates. The pathway circumvents the cytotoxicity issue caused by methylglyoxal intermediate in the naturally existing pathway. Successful E. coli bioconversion of lactic acid to 1,2-PDO was shown in previous report. Here, we demonstrated the engineering of E. coli host strains for the de novo biosynthesis of 1,2-PDO through this pathway. Under fermenter-controlled conditions, the R-1,2-PDO was produced at 17.3 g/L with a molar yield of 42.2% from glucose, while the S-isomer was produced at 9.3 g/L with a molar yield of 23.2%. The optical purities of the two isomers were 97.5% ee (R) and 99.3% ee (S), respectively. To the best of our knowledge, these are the highest titers of 1,2-PDO biosynthesized by either natural producer or engineered microbial strains that are published in peer-reviewed journals