Soluble solid content (SSC), pH, and vitamin C (VC) are considered as key parameters for strawberry quality. Spectral, color, and textural features from hyperspectral reflectance imaging of 400–1000 nm was to develop the non-destructive detection approaches for SSC, pH, and VC of strawberries by integrating various multivariate methods as partial least-squares regression (PLSR), support vector regression, and locally weighted regression (LWR). SSC, pH, and VC of 120 strawberries were statistically analyzed to facilitate the partitioning of data sets, which helped optimize the model. PLSR, with spectral and color features, obtained the optimal prediction of SSC with determination coefficient of prediction (Rp2) of 0.9370 and the root mean square error of prediction (RMSEP) of 0.1145. Through spectral features, the best prediction for pH was obtained by LWR with Rp2 = 0.8493 and RMSEP = 0.0501. Combination of spectral and textural features with PLSR provided the best results of VC with Rp2 = 0.8769 and RMSEP = 0.0279. Competitive adaptive reweighted sampling and uninformative variable elimination (UVE) were used to select important variables from the above features. Based on the important variables, the accuracy of SSC, pH, and VC prediction both gain the promotion. Finally, the distribution maps of SSC, pH, and VC over time were generated, and the change trend of three quality parameters was observed. Thus, the proposed method can nondestructively and accurately determine SSC, pH, and VC of strawberries and is expected to design and construct the simple sensors for the above quality parameters of strawberries.
Previous researches have demonstrated deep learning models' vulnerabilities to decision-based adversarial attacks, which craft adversarial examples based solely on information from output decisions (top-1 labels). However, existing decision-based attacks have two major limitations, i.e., expensive query cost and being easy to detect. To bridge the gap and enlarge real threats to commercial applications, we propose a novel and efficient decision-based attack against black-box models, dubbed FastDrop, which only requires a few queries and work well under strong defenses. The crux of the innovation is that, unlike existing adversarial attacks that rely on gradient estimation and additive noise, FastDrop generates adversarial examples by dropping information in the frequency domain. Extensive experiments on three datasets demonstrate that FastDrop can escape the detection of the state-of-the-art (SOTA) black-box defenses and reduce the number of queries by 13~133× under the same level of perturbations compared with the SOTA attacks. FastDrop only needs 10~20 queries to conduct an attack against various black-box models within 1s. Besides, on commercial vision APIs provided by Baidu and Tencent, FastDrop achieves an attack success rate (ASR) of 100% with 10 queries on average, which poses a real and severe threat to real-world applications.
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