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Teaching Adversarial Thinking for Cybersecurity A better understanding of the concept of adversarial thinking is needed in order to improve this aspect of cybersecurity education This paper sheds new light on adversarial thinking by exploring it through the lens of Sternberg’s triarchic theory of intelligence
arXiv:1611. 01236v2 [cs. CV] 11 Feb 2017 Adversarial examples pose potential security threats for practical machine learning applications In particular, Szegedy et al (2014) showed that an adversarial example that was designed to be misclassified by a model M1 is often also misclassified by a model M2 This adversarial example transferability property means that it is possible to generate adversarial examples and perform a mis
Co-Evolving Complexity: An Adversarial Framework for . . . Learned Adversarial Procedural Generation for Multi-Agent Curricula We reframe the problem of environment design as a two-player game between a generative Attacker and a team of cooperative Defender agents The Attacker’s goal is to learn a policy for procedurally generating sequences of hostile units (i e , worlds or challenges) that can defeat the Defenders Simultaneously, the multi
Adversarial Self-Supervised Contrastive Learning - NeurIPS Figure 1: Overview of our adversarial contrastive self-supervised learning (a) We generate instance-wise adversarial examples from an image transformed using a stochastic augmentation, which makes the model confuse the instance-level identity of the perturbed sample (b) We then maximize the similarity between each transformed sample and their instance-wise adversaries using contrastive
Litigation\IR Adversarial procedure was defended for its closer proximity to “dialectical” models, with emphasis on assertion and refutation, and yet attacked by enlightened rationalists, generally skeptical of information provided by biased and self-interested actors 7 In the evolved conception of the adversarial procedure, the parties’ attorneys
Impacts of Adversarial Use of Generative AI on Homeland Security Whole of Government Responses: U S Government officials across relevant agencies and departments need to define respective roles in the event of a real-time, foreign adversary launched GenAI attack For robust response, these roles would best be defined in the context of multiple scenario-based narratives, and consider a broad scope of attacks and possible impacts
Conditional Adversarial Domain Adaptation Abstract Adversarial learning has been embedded into deep networks to learn disentangled and transferable representations for domain adaptation Existing adversarial do-main adaptation methods may not effectively align different domains of multimodal distributions native in classification problems In this paper, we present conditional adversarial domain adaptation, a principled framework that