On the Real-World Adversarial Robustness of Real-Time Semantic Segmentation Models for Autonomous Driving.

The existence of real-world adversarial examples (RWAEs) (commonly in the form of patches) poses a serious threat for the use of deep learning models in safety-critical computer vision tasks such as visual perception in autonomous driving. This article presents an extensive evaluation of the robustness of semantic segmentation (SS) models when attacked with different types of adversarial patches, including digital, simulated, and physical ones. A novel loss function is proposed to improve the capabilities of attackers in inducing a misclassification of pixels.

On the Minimal Adversarial Perturbation for Deep Neural Networks With Provable Estimation Error.

Although Deep Neural Networks (DNNs) have shown incredible performance in perceptive and control tasks, several trustworthy issues are still open. One of the most discussed topics is the existence of adversarial perturbations, which has opened an interesting research line on provable techniques capable of quantifying the robustness of a given input. In this regard, the euclidean distance of the input from the classification boundary denotes a well-proved robustness assessment as the minimal affordable adversarial perturbation.

Detecting Adversarial Examples by Input Transformations, Defense Perturbations, and Voting.

Over the past few years, convolutional neural networks (CNNs) have proved to reach superhuman performance in visual recognition tasks. However, CNNs can easily be fooled by adversarial examples (AEs), i.e.

Development and validation of a behavioural video coding scheme for detecting mental workload in manual assembly.

Manual assembly in the future workplace will put high demands on operators' cognitive processing. The development of mental workload (MWL) measures therefore looms large. Physiological gauges such as electroencephalography (EEG) show promising possibilities, but still lack sufficient reliability when applied in the field.

Impact of digital dermoscopy analysis on the decision to follow up or to excise a pigmented skin lesion: a multicentre study.

Background: The quality of early malignant melanoma (MM) diagnosis is dependent on the experience of dermatologists, tools like dermoscopy and histopathology, and awareness and education of the studied population. Does a higher rate of excision of pigmented skin lesions (PSL) increase the rate of detected melanomas?

Material And Methods: The DB-MIPS objective tool, able to evaluate mathematical defined variables, has been used to verify the variability of measurements among PSL stored by five different centres located in Italy, Switzerland, and Germany.

Results: The objective analysis showed low differences in terms of moles' features among the different groups, arguing for robustness of the dermatological patient's PSL inspection.

Lymphocytapheresis in the treatment of psoriasis vulgaris.

Psoriasis is a common autoimmune chronic inflammatory skin disease that affects approximately 2% of the world's population; fundamental for its immunopathogenic mechanism is secretion of type 1 (Th1) cytokines by T cells and their activation. Since cytapheresis has been widely applied to autoimmune disorders, emphasizing the recently reported results of granulocyte and monocyte adsorption apheresis in psoriasis, a small series of psoriasis vulgaris (PV) patients underwent lymphocytapheresis (LCA) with the aim to remove lymphocytes. Five patients were submitted to weekly LCA.