MediaEval 2019: concealed FGSM perturbations for privacy preservation

This work tackles the Pixel Privacy task put forth by MediaEval 2019. Our goal is to decrease the accuracy of a classification algorithm while preserving the original image quality. We use the fast gradient sign method, which normally has a corrupting influence on image appeal, and devise two methods to minimize the damage. The first approach uses a map that is a combination of salient and flat areas. Perturbations are more noticeable in these locations, and so are directed away from them. The second approach adds the gradient of an aesthetic algorithm to the gradient of the attacking algorithm to guide the perturbations towards a direction that preserves appeal. We make our code available at: https://git.io/JesX

Adversarial Color Enhancement: Generating Unrestricted Adversarial Images by Optimizing a Color Filter

We introduce an approach that enhances images using a color filter in order to create adversarial effects, which fool neural networks into misclassification. Our approach, Adversarial Color Enhancement (ACE), generates unrestricted adversarial images by optimizing the color filter via gradient descent. The novelty of ACE is its incorporation of established practice for image enhancement in a transparent manner. Experimental results validate the white-box adversarial strength and black-box transferability of ACE. A range of examples demonstrates the perceptual quality of images that ACE produces. ACE makes an important contribution to recent work that moves beyond $L_p$ imperceptibility and focuses on unrestricted adversarial modifications that yield large perceptible perturbations, but remain non-suspicious, to the human eye. The future potential of filter-based adversaries is also explored in two directions: guiding ACE with common enhancement practices (e.g., Instagram filters) towards specific attractive image styles and adapting ACE to image semantics. Code is available at https://github.com/ZhengyuZhao/ACE.Comment: Accepted by BMVC 2020. Code is available at https://github.com/ZhengyuZhao/AC

The Geo-Privacy Bonus of Popular Photo Enhancements

Contains fulltext : 181494.pdf (publisher's version ) (Closed access)ICMR '17: 2017 ACM on International Conference on Multimedia Retrieval, Bucharest, Romania — June 06 - 09, 201

Geoprivacidade, pegada digital e vida online…

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