Adversarial attacks on machine learning models have seen increasing interest in the past years. By making only subtle changes to the input of a convolutional neural network, the output of the network can be swayed to output a completely different result. The first attacks did this by changing pixel values of an input image slightly to fool a classifier to output the wrong class. Other approaches have tried to learn "patches" that can be applied to an object to fool detectors and classifiers. Some of these approaches have also shown that these attacks are feasible in the realworld, i.e. by modifying an object and filming it with a video camera. However, all of these approaches target classes that contain almost no intra-class variety (e.g. stop signs). The known structure of the object is then used to generate an adversarial patch on top of it. Figure 1:We create an adversarial patch that is successfully able to hide persons from a person detector. Left: The person without a patch is successfully detected. Right: The person holding the patch is ignored.
ABSTRACT:In this work, we compare four different approaches for detecting photovoltaic installations from RGB aerial images. Our client, an electricity grid administrator, wants to hunt down fraud with unregistered illegal solar panel installations by detecting installations in aerial imagery and checking these against their database of registered installations. The detection of solar panels in these RGB images is a difficult task. Reasons are the relatively low resolution (at 25 cm/pixel an individual solar panel only measures about 9 × 7 pixels), the undiscriminating colour properties of the object (due to in-class variance and specular effects) and the apparent shape variability (rotation and skew due to the different roofs slant angles). Therefore, straightforward object segmentation techniques do not yield a satisfying solution, as proven in this paper. We compared four state-of-the-art object detection approaches for this task. First we experimented with a machine learning object detection technique based on pixel-based support vector machine classification. Secondly we developed an approach using MSER based colour segmentation and shape analysis. Finally a dual approach based on object categorization using the boosted cascade classifier technique of Viola & Jones and the aggregate channel features technique of Dollár et al., is introduced, learning a combination of colour and gradient feature based classifiers from a given training set. We successfully evaluate these four different approaches on a fully labelled test set of a 8000 × 8000 pixel, 4 square km zone containing 315 solar panel installations with in total more than 10.000 individual panels.
A traditional re-identification pipeline consists of a detection and re-identification step, i.e. a person detector is run on an input image to get a cutout which is then sent to a separate re-identification system. In this work we combine detection and re-identification into one single pass neural network. We propose an architecture that can do re-identification simultaneously with detection and classification. The effect of our modification has only a negligible impact on detection accuracy, and adds the calculation of re-identification vectors at virtually no cost. The resulting re-identification vector is strong enough to be used in speed sensitive applications which can benefit from an additional re-identification vector in addition to detection. We demonstrate this by using it as detection and re-identification input for a real-time person tracker. Moreover, unlike traditional detection + re-id pipelines our single-pass network's computational cost is not dependent on the number of people in the image.
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