Adversarial Examples in Constrained DomainsRyan SheatsleyNicolas PapernotMichael J. WeismanGunjan VermaPatrick D. McDaniel
and Δ is an allowable set of perturbations. In practice, adversarial examples will always be designed to be as unconscious as possible to the human eye. One commonly define the allowed perturbations set Δ to be a hypersphere ball around any dataxwith a constrained norm (e...
fake, as illustrated in Fig.1. The resulting adversarial network, GAN, has demonstrated huge capability for data generation and has gained much attention in image classification and proven relevant in domains such as adaptation, data augmentation, and image-to-image translation10. Figure 1 Illustrati...
Adversarial Examples in Constrained Domains. arXiv 2020, arXiv:2011.01183. [Google Scholar] Refaeilzadeh, P.; Tang, L.; Liu, H. Cross-validation. Encycl. Database Syst. 2009, 5, 532–538. [Google Scholar] Bai, T.; Luo, J.; Zhao, J.; Wen, B. Recent Advances in Adversarial ...
In the realm of RF signals, Silvija et al. [22] took a statistical approach toward adversarial detection, although with a constrained experimental scope. In essence, while AE-based techniques show promise, direct applications from the image to signal domains are ineffective. There is an emerging...
It can be hard to stay up-to-date on the published papers in the field of adversarial examples, where we have seen massive growth in the number of papers written each year. I have been somewhat religiously keeping track of these papers for the last few years, and realized it may be ...
However, deep neural networks have been recently found vulnerable to well-designed input samples, called adversarial examples. Adversarial examples are imperceptible to human but can easily fool deep neural networks in the testing/deploying stage. The vulnerability to adversarial examples becomes one of ...
Examples of applications of GAN in ophthalmology image domains.aPost-intervention prediction for decompression surgery for thyroid ophthalmopathy [15] and anti-vascular endothelial growth factor (VEGF) therapy for neovascular age-related macular degeneration [66].bDenoising in fundus photography [53] and...
This step further improves the model's performance by incorporating some labeled examples. 5. Benefits and Challenges of Datafree Adversarial Distillation The benefits of datafree adversarial distillation include the ability to transfer knowledge without the need for labeled data, which is often ...
We present an approach for learning to translate an image from a source domain X to a target domain Y in the absence of paired examples. Our goal is to learn a mapping G:X→Y such that the distribution of images from G(X) is indistinguishable from the distribution Y using an ...