Convolutional Neural Networks have achieved significant success across multiple computer vision tasks. However, they are vulnerable to carefully crafted, human-imperceptible adversarial noise patterns which constrain their deployment in critical security-sensitive systems. This paper proposes a computationally efficient image enhancement approach that provides strong defense mechanism effectively mitigate the effect of such perturbations. We show deep restoration networks learn mapping...

Deep neural networks are vulnerable to adversarial attacks which can fool them by adding minuscule perturbations the input images. The robustness of existing defenses suffers greatly under white-box attack settings, where an adversary has full knowledge about network and iterate several times find strong perturbations. We observe that main reason for existence such is close proximity different class samples in learned feature space. This allows model decisions be totally changed...

Deep neural networks can easily be fooled by an adversary with minuscule perturbations added to input image. The existing defense techniques suffer greatly under white-box attack settings, where has full knowledge of the network and iterate several times find strong perturbations. We observe that main reason for existence such vulnerabilities is close proximity different class samples in learned feature space deep models. This allows model decisions completely changed adding imperceptible...

The choice of a loss function is an important factor when training neural networks for image restoration problems, such as single super resolution. should encourage natural and perceptually pleasing results. A popular pre-trained network, VGG, which used feature extractor computing the difference between restored reference images. However, approach has multiple drawbacks: it computationally expensive, requires regularization hyper-parameter tuning, involves large network trained on unrelated...

Deep neural networks are vulnerable to adversarial attacks, which can fool them by adding minuscule perturbations the input images. The robustness of existing defenses suffers greatly under white-box attack settings, where an adversary has full knowledge about network and iterate several times find strong perturbations. We observe that main reason for existence such is close proximity different class samples in learned feature space. This allows model decisions be totally changed...

Many image enhancement or editing operations, such as forward and inverse tone mapping color grading, do not have a unique solution, but instead range of solutions, each representing different style. Despite this, existing learning-based methods attempt to learn mapping, disregarding this In work, we show that information about the style can be distilled from collections pairs encoded into 2- 3-dimensional vector. This gives us only an efficient representation also interpretable latent space...

Automated facial video analysis is useful in numerous health care applications. For example, spatio-temporal of such videos has been previously done for assisting clinicians the diagnosis depression. Physiological measures, as an individual's heart rate, provide very important cues to understand a person's mental health. Unobtrusively estimated rate not used analyse individuals' In this paper, we automatically estimate activity from videos. We then study association with health, diagnosed by...

Image enhancement and image retouching processes are often dominated by global (shift-invariant) change of colour tones. Most "deep learning" based methods proposed for trained to enforce similarity in pixel values and/or the high-level feature space. We hypothesise that tasks, such as retouching, which involve a significant shift statistics, training model restore overall distribution can be vital importance. To address this, we study effect Histogram Matching loss function on state-of-the...

Scarcity of labeled data has motivated the development semi-supervised learning methods, which learn from large portions unlabeled alongside a few samples. Consistency Regularization between model's predictions under different input perturbations, particularly shown to provide state-of-the art results in framework. However, most these method have been limited classification and segmentation applications. We propose Transformation Regularization, delves into more challenging setting...

The choice of a loss function is an important factor when training neural networks for image restoration problems, such as single super resolution. should encourage natural and perceptually pleasing results. A popular pre-trained network, VGG, which used feature extractor computing the difference between restored reference images. However, approach has multiple drawbacks: it computationally expensive, requires regularization hyper-parameter tuning, involves large network trained on unrelated...