Machine learning classifiers for image recognition are prevalent in many applications. We study the problem of finding adversarial examples for such classifiers, i.e., to manipulate the images in such a way that they still look like the original images to a human but are misinterpreted by the classifier. Finding adversarial examples corresponds to a search problem in the image space. We focus on black-box attacks that can only use the original classifier to guide the search. The challenge is not to find adversarial examples, but rather to find them efficiently, ideally in real time. We show two novel methods that increase the efficiency of black-box search algorithms for adversarial examples: The first uses a relevance mask, i.e., a bitmask on the original image that restricts the search to those pixels that appear to be more relevant to the attacked classifier than others. The second exploits the discovery of merge drift, a phenomenon that negatively affects search algorithms that are based on the merging of image candidates. We evaluate both concepts on existing and new algorithms.
In view of the advent of autonomous driving, the automotive industry is longing for adequately trained employees in a wide range of fields; these include not so much the classic automotive engineering and embedded systems topics, but rather software development, robotics, machine learning, statistics and design. As a particularly specific, industry-oriented discipline, automotive software engineering is hardly represented in academic teaching, except for a few AUTOSAR courses. But as a beacon project of digitization, autonomous driving can arouse broad interest among students who are interested in how diverse disciplines can be profitably combined to realize an impressive project. This interest is an excellent chance for both universities and industry to engage in constructive dialogue, to carry out exciting multidisciplinary projects at universities and to meet the needs of industry with graduates who have a relevant and versatile education. This workshop provides an opportunity for both sides to meet, share requirements and experiences and develop a best practice course according to industry requirements and academic feasibility.
