Uncertainty-based Detection of Adversarial Attacks in Semantic Segmentation

State-of-the-art deep neural networks have proven to be highly powerful in a broad range of tasks, including semantic image segmentation. However, these networks are vulnerable against adversarial attacks, i.e., non-perceptible perturbations added to the input image causing incorrect predictions, which is hazardous in safety-critical applications like automated driving. Adversarial examples and defense strategies are well studied for the image classification task, while there has been limited research in the context of semantic segmentation. First works however show that the segmentation outcome can be severely distorted by adversarial attacks. In this work, we introduce an uncertainty-based approach for the detection of adversarial attacks in semantic segmentation. We observe that uncertainty as for example captured by the entropy of the output distribution behaves differently on clean and perturbed images and leverage this property to distinguish between the two cases. Our method works in a light-weight and post-processing manner, i.e., we do not modify the model or need knowledge of the process used for generating adversarial examples. In a thorough empirical analysis, we demonstrate the ability of our approach to detect perturbed images across multiple types of adversarial attacks

Uncertainty-weighted Loss Functions for Improved Adversarial Attacks on Semantic Segmentation

State-of-the-art deep neural networks have been shown to be extremely powerful in a variety of perceptual tasks like semantic segmentation. However, these networks are vulnerable to adversarial perturbations of the input which are imperceptible for humans but lead to incorrect predictions. Treating image segmentation as a sum of pixel-wise classifications, adversarial attacks developed for classification models were shown to be applicable to segmentation models as well. In this work, we present simple uncertainty-based weighting schemes for the loss functions of such attacks that (i) put higher weights on pixel classifications which can more easily perturbed and (ii) zero-out the pixel-wise losses corresponding to those pixels that are already confidently misclassified. The weighting schemes can be easily integrated into the loss function of a range of well-known adversarial attackers with minimal additional computational overhead, but lead to significant improved perturbation performance, as we demonstrate in our empirical analysis on several datasets and models

Detection of False Positive and False Negative Samples in Semantic Segmentation

Rottmann M, Maag K, Chan RK-W, Huger F, Schlicht P, Gottschalk H. Detection of False Positive and False Negative Samples in Semantic Segmentation. In: 2020 Design, Automation & Test in Europe Conference & Exhibition (DATE). IEEE; 2020: 1351-1356

Two Video Data Sets for Tracking and Retrieval of Out of Distribution Objects

Maag K, Chan RK-W, Uhlemeyer S, Kowol K, Gottschalk H. Two Video Data Sets for Tracking and Retrieval of Out of Distribution Objects. In: Wang L, Gall J, Chin T-J, Sato I, Chellappa R, eds. Computer Vision – ACCV 2022. 16th Asian Conference on Computer Vision, Macao, China, December 4–8, 2022, Proceedings, Part V. Lecture Notes in Computer Science. Vol 13845. Cham: Springer Nature Switzerland; 2023: 476-494.In this work we present two video test data sets for the novel computer vision (CV) task of out of distribution tracking (OOD tracking). Here, OOD objects are understood as objects with a semantic class outside the semantic space of an underlying image segmentation algorithm, or an instance within the semantic space which however looks decisively different from the instances contained in the training data. OOD objects occurring on video sequences should be detected on single frames as early as possible and tracked over their time of appearance as long as possible. During the time of appearance, they should be segmented as precisely as possible. We present the SOS data set containing 20 video sequences of street scenes and more than 1000 labeled frames with up to two OOD objects. We furthermore publish the synthetic CARLA-WildLife data set that consists of 26 video sequences containing up to four OOD objects on a single frame. We propose metrics to measure the success of OOD tracking and develop a baseline algorithm that efficiently tracks the OOD objects. As an application that benefits from OOD tracking, we retrieve OOD sequences from unlabeled videos of street scenes containing OOD objects

Coherent cyclotron motion beyond Kohn’s theorem

In solids, the high density of charged particles makes many-body interactions a pervasive principle governing optics and electronics(1-12). However, Walter Kohn found in 1961 that the cyclotron resonance of Landau-quantized electrons is independent of the seemingly inescapable Coulomb interaction between electrons(2). Although this surprising theorem has been exploited in sophisticated quantum phenomena(13-15), such as ultrastrong light-matter coupling(16), superradiance(17) and coherent control(18), the complete absence of nonlinearities excludes many intriguing possibilities, such as quantum-logic protocols(19). Here, we use intense terahertz pulses to drive the cyclotron response of a two-dimensional electron gas beyond the protective limits of Kohn's theorem. Anharmonic Landau ladder climbing and distinct terahertz four-and six-wave mixing signatures occur, which our theory links to dynamic Coulomb effects between electrons and the positively charged ion background. This new context for Kohn's theorem unveils previously inaccessible internal degrees of freedom of Landau electrons, opening up new realms of ultrafast quantum control for electrons