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Found 5 papers, 2 papers with code
On the limits of perceptual quality measures for enhanced underwater images
The appearance of objects in underwater images is degraded by the selective attenuation of light, which reduces contrast and causes a colour cast.
On the reversibility of adversarial attacks
We refer to this property as the reversibility of an adversarial attack, and quantify reversibility as the accuracy in retrieving the original class or the true class of an adversarial example.
Training privacy-preserving video analytics pipelines by suppressing features that reveal information about private attributes
However, the features extracted by a deep neural network that was trained to predict a specific, consensual attribute (e. g. emotion) may also encode and thus reveal information about private, protected attributes (e. g. age or gender).
Underwater image filtering: methods, datasets and evaluation
The growing interest in underwater image filtering methods--including learning-based approaches used for both restoration and enhancement--and the associated challenges call for a comprehensive review of the state of the art.
Exploiting vulnerabilities of deep neural networks for privacy protection
To address these limitations, we present an adversarial attack {that is} specifically designed to protect visual content against { unseen} classifiers and known defenses.
