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The massive photos shared through the social networks nowadays, e.g., Facebook and Instagram, have aided malicious entities to snoop private information, especially by utilizing deep neural networks (DNNs) to learn from those personal photos. To protect photo privacy against DNNs, recent advances adopting adversarial examples could successfully fool DNNs. However, they are sensitive to those image compression methods that are commonly used on social networks to reduce transmission bandwidth or storage space. A recent work proposed to resist JPEG compression, while the compression methods adopted in social networks are black boxes, and variation of compression methods would significantly degrade the resistance. To the best of our knowledge, this paper gives the first attempt to investigate a generic compression-resistant scheme to protect photo privacy against DNNs in the social network scenario. We propose the Compression-Resistant Adversarial framework (ComReAdv) that can achieve adversarial examples robust to an unknown compression method. To this end, we design an encoding-decoding based compression approximation model (ComModel) to approximate the unknown compression method by learning the transformation from the original-compressed pairs of images queried through the social network. In addition, we involve the pre-trained differentiable ComModel into the optimization process of adversarial example generation and adapt existing attack algorithms to generate compression-resistant adversarial examples. Extensive experimental results on different social networks demonstrate the effectiveness and superior resistance of the proposed ComReAdv to unknown compression as compared to the state-of-the-art methods.

In order to optimize the extraction of gigantol from Dendrobium officinale, the influence of methanol concentration, ultrasonic temperature, and liquid ratio on extraction efficiency was analysed by the response surface analysis method. The results show that the extraction rate reached a maximum when the methanol concentration was 92.98%, the solid-liquid ratio was 27.2 mL/g, and the extraction temperature was 41.41 °C. The content of gigantol of Dendrobium officinale in leaves was significantly higher than that in stems, reaching 4.7942 μg/g. The content of gigantol in Dendrobium huoshanensis Fengdou was significantly higher than that of other species of Fengdou. This experiment has practical significance for improving the utilization rate of Dendrobium officinale, and provides a reference for the study of the pharmacological and biological activity of gigantol.

Recent studies demonstrated that deep neural networks (DNNs) are vulnerable to adversarial examples, which would seriously threaten security-sensitive applications. Existing works synthesized the adversarial examples by perturbing the original/benign images by leveraging the $\mathcal {L}_p$ L p -norm to penalize the perturbations, which restricts the pixel-wise distance between the adversarial images and correspondingly benign images. However, they added perturbations globally to the benign images without explicitly considering their content/spacial structure, resulting in noticeable artifacts especially in those originally clean regions, e.g., sky and smooth surface. In this paper, we propose an invisible adversarial attack, which synthesizes adversarial examples that are visually indistinguishable from benign ones. We adaptively distribute the perturbation according to human sensitivity to a local stimulus in the benign image, i.e., the higher insensitivity, the more perturbation. Two types of adaptive adversarial attacks are proposed: 1) coarse-grained and 2) fine-grained. The former conducts $\mathcal {L}_p$ L p -norm regularized by the novel spatial constraints, which utilizes the rich information of the cluttered regions to mask perturbation. The latter, called Just Noticeable Distortion (JND)-based adversarial attack, utilizes the proposed JND $_p$ p metric for better measuring the perceptual similarity, and adaptively sets penalty by weighting the pixel-wise perceptual redundancy of an image. We conduct extensive experiments on the MNIST, CIFAR-10 and ImageNet datasets and a comprehensive user study with 50 participants. The experimental results demonstrate that JND $_p$ p is a better metric for measuring the perceptual similarity than $\mathcal {L}_p$ L p -norm, and the proposed adaptive adversarial attacks can synthesize indistinguishable adversarial examples from benign ones and outperform the state-of-the-art methods.

Alkaloids are the main active ingredients in the medicinal plant Dendrobium officinale. Based on the published genomic and transcriptomic data, a proposed terpenoid indole alkaloid (TIA) biosynthesis pathway may be present in D. officinale. In this study, protocorm-like bodies (PLBs) with a high-yielding production of alkaloids were obtained by the optimization of tryptophan, secologanin and methyl jasmonate (MeJA) treatment. The results showed that the total alkaloid content was 2.05 times greater than that of the control group when the PLBs were fed with 9 µM tryptophan, 6 µM secologanin and 100 µM MeJA after 36 days. HPLC analysis showed that strictosidine synthase (STR) activity also increased in the treated plants. A total of 78 metabolites were identified using gas chromatography-mass spectrometry (GC-MS) in combination with liquid chromatography-mass spectrometry (LC-MS) methods; 29 differential metabolites were identified according to the multivariate statistical analysis. Among them, carapanaubine, a kind of TIA, exhibited dramatically increased levels. In addition, a possible underlying process of the metabolic flux from related metabolism to the TIA biosynthetic pathway was enhanced. These results provide a comprehensive view of the metabolic changes related to alkaloid biosynthesis, especially TIA biosynthesis, in response to tryptophan, secologanin and MeJA treatment.