In conventional machine learning systems,adversarial examplesare inputs that have been manipulated and prepared to force the system to make incorrect classification and make difficulties in semantic segmentation. This type of attack, that causes problems in various domains such as spam filters, intrusion...
Adversarial Examples in Constrained DomainsRyan SheatsleyNicolas PapernotMichael J. WeismanGunjan VermaPatrick D. McDaniel
Chapter 16 Adversarial Examples in Image Forensics Mauro Barni, Wenjie Li, Benedetta Tondi, and Bowen Zhang Abstract We describe the threats posed by adversarial examples in an image forensic context, highlighting the differences and similarities with respect to other applica- tion domains. Particular ...
Therefore, there is an urgent need to design effective defensive approaches to defend against such adversarial examples in order to ensure the safety and reliability of computer vision applications. To improve the adversarial robustness of DNNs, several defence methods have been proposed, which can be...
In domain adaptation, this prin- ciple has been employed to ensure that the network cannot distinguish between the distributions of its training and test domain examples [11, 12, 13]. However, each algorithm makes different design choices such as whether to use a gen- erator, which loss ...
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 ...
Present a likelihood-free method to estimate parameters in implicit models. It is to approximate the result of maximizing the likelihood. The assumptions: the capacity of the model is finite; the number of data examples is finite. The proposed method relies on the following observation: a model...
Examples of such AEs are presented in the last column of Fig. 1. One can see e.g. that the system has problems when random dots appear around a 0 and misclassifies the input as the number 5. While the attacker will aim to accomplish many successful attacks, the postal service will...
Deep neural networks (DNNs) display good performance in the domains of recognition and prediction, such as on tasks of image recognition, speech recognition, video recognition, and pattern analysis. However, adversarial examples, created by inserting a small amount of noise into the original samples...
that the narrow attack surface exposed by constrained domains is still sufficiently large to craft successful adversarial examples; and thus, constraints do not appear to make a domain robust. Indeed, with as l