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Early detection of Alzheimer's Disease (AD) is important so that preventative measures can be taken. Current techniques for detecting AD rely on cognitive impairment testing which unfortunately does not yield accurate diagnoses until the patient has progressed beyond a moderate AD. In this project, we develop a new approach based on mathematical and image processing techniques for better classification of AD. The most popular current technique analyzes MRI scans using properties of diffeomorphism which generates a mapping from one MRI to another. Since MRIs are very high dimensional vector spaces, the existing technique reduces it to three dimensions and then clusters the images according to presence or lack of AD. However, reducing a high dimensional vector space to three dimensions compromises the information in the data and thus results in some loss of accuracy. We propose to reduce the high dimensional MRI image vector space to 150 dimensions using Principal Component Analysis. In order to categorize the reduced dimensions from PCA for progression of AD, we employ a multiclass neural network. The neural network is trained initially on 230 diagnosed MRIs obtained from OASIS MRI database. We then test our trained neural network on the entire set of 457 MRIs provided by OASIS dataset to confirm the accuracy of diagnosis by our system. Our results produce nearly 90% accuracy in AD diagnosis and classification.

The field of adversarial machine learning has experienced a near exponential growth in the amount of papers being produced since 2018. This massive information output has yet to be properly processed and categorized. In this paper, we seek to help alleviate this problem by systematizing the recent advances in adversarial machine learning black-box attacks since 2019. Our survey summarizes and categorizes 20 recent black-box attacks. We also present a new analysis for understanding the attack success rate with respect to the adversarial model used in each paper. Overall, our paper surveys a wide body of literature to highlight recent attack developments and organizes them into four attack categories: score based attacks, decision based attacks, transfer attacks and non-traditional attacks. Further, we provide a new mathematical framework to show exactly how attack results can fairly be compared. INDEX TERMSAdversarial machine learning, adversarial examples, adversarial defense, black-box attack, security, deep learning. Score based Attacks Attack Name Date Author qMeta 6-Jun-19 Du et al. [24] P-RGF 17-Jun-19 Cheng et al. [25] ZO-ADMM 26-Jul-19 Zhao et al. [26] TREMBA 17-Nov-19 Huang et al. [27] Square 29-Nov-19 Andriushchenko et al. [28] ZO-NGD 18-Feb-20 Zhao et al. [29] PPBA 8-May-20 Liu et al. [30] Decision based Attacks Attack Name Date Author qFool 26-Mar-19 Liu et al. [31] HSJA 3-Apr-19 Chen et al. [10] GeoDA 13-Mar-20 Rahmati et al. [32] QEBA 28-May-20 Li et al. [33] RayS 23-Jun-20 Chen et al. [34] SurFree 25-Nov-20 Maho et al. [12] NonLinear-BA 25-Feb-21 Li et al. [35] Transfer based Attacks Attack Name Date Author Adaptive 3-Oct-19 Mahmood et al. [22] DaST 28-Mar-20 Zhou et al. [9] PO-TI 13-Jun-20 Li et al. [23] Non-traditional Attacks Attack Name Date Author CornerSearch 11-Sep-19 Croce et al. [36] ColorFool 25-Nov-19 Shamsabadi et al. [38] Patch 12-Apr-20 Yang et al. [37]

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