Towards Practical Verification of Machine Learning: The Case of Computer Vision Systems

Due to the increasing usage of machine learning (ML) techniques in security- and safety-critical domains, such as autonomous systems and medical diagnosis, ensuring correct behavior of ML systems, especially for different corner cases, is of growing importance. In this paper, we propose a generic framework for evaluating security and robustness of ML systems using different real-world safety properties. We further design, implement and evaluate VeriVis, a scalable methodology that can verify a diverse set of safety properties for state-of-the-art computer vision systems with only blackbox access. VeriVis leverage different input space reduction techniques for efficient verification of different safety properties. VeriVis is able to find thousands of safety violations in fifteen state-of-the-art computer vision systems including ten Deep Neural Networks (DNNs) such as Inception-v3 and Nvidia's Dave self-driving system with thousands of neurons as well as five commercial third-party vision APIs including Google vision and Clarifai for twelve different safety properties. Furthermore, VeriVis can successfully verify local safety properties, on average, for around 31.7% of the test images. VeriVis finds up to 64.8x more violations than existing gradient-based methods that, unlike VeriVis, cannot ensure non-existence of any violations. Finally, we show that retraining using the safety violations detected by VeriVis can reduce the average number of violations up to 60.2%.Comment: 16 pages, 11 tables, 11 figure

On fast and accurate detection of unauthorized wireless access points using clock skews

Journal ArticleWe explore the use of clock skew of a wireless local area network access point (AP) as its fingerprint to detect unauthorized APs quickly and accurately. The main goal behind using clock skews is to overcome one of the major limitations of existing solutions-the inability to effectively detect Medium Access Control (MAC) address spoofing. We calculate the clock skew of an AP from the IEEE 802.11 Time Synchronization Function (TSF) time stamps sent out in the beacon/probe response frames. We use two different methods for this purpose-one based on linear programming and the other based on least-square fit. We supplement these methods with a heuristic for differentiating original packets from those sent by the fake APs. We collect TSF time stamp data from several APs in three different residential settings. Using our measurement data as well as data obtained from a large conference setting, we find that clock skews remain consistent over time for the same AP but vary significantly across APs. Furthermore, we improve the resolution of received time stamp of the frames and show that with this enhancement, our methodology can find clock skews very quickly, using 50-100 packets in most of the cases. We also discuss and quantify the impact of various external factors including temperature variation, virtualization, clock source selection, and NTP synchronization on clock skews. Our results indicate that the use of clock skews appears to be an efficient and robust method for detecting fake APs in wireless local area networks

A CRITICAL ANALYSIS ON THE BASIC CONCEPT OF PRAMEHA IN CONVENTIONAL PARLANCE

Systemic information about several diseases has been documented in Ayurvedic classical texts. Although conventional entity of all of those diseases are not well established. Understanding of such diseases in conventional parlance is essential for an evidence based approach of Ayurveda. Prameha is one of such disease that is most widely described in almost all classical Ayurvedic texts but not well established in conventional parlance. The disease Premeha, has been named on its major clinical signs Avila-Prabhuta-Mutra (Excess and contaminated urine). In ancient text compiled by Acharya Charaka, Acharya Sushruta, Acharya Vagbhatta and many others, we get detailed description about this disease. Meda Dusti is considered as a key pathological phenomenon behind the development of Prameha. A conventional entity of this disease is still now doubtful. This review aims at scanning of both Ayurvedic and conventional medical literatures as well as published research articles to explore the basic concept of Prameha in conventional parlance