Adversarial Attacks News Today

Deepfakes are threatening privacy and security. Detection methods using deep learning aim to combat this but there’s a long way to go.

Spotlight News: Join the Advanced Certification Course in Automated & Electric Vehicles by Nexus at University of Michigan to drive the future of auto tech forward. Learn about the major aspects of electric vehicles and explore global cutting-edge research and development projects on connected and automated vehicles.

By harnessing AI capabilities to strengthen and reinforce defense systems, organizations could enhance resilience and protect against evolving cybersecurity threats.

A professionally curated list of papers, tutorials, books, videos, articles and open-source libraries etc for Out-of-distribution detection, robustness, and generalization - GitHub - continuousml/Awesome-Out-Of-Distribution-Detection: A professionally curated list of papers, tutorials, books, videos, articles and open-source libraries etc for Out-of-distribution detection, robustness, and generalization

PhotoGuard is AI technique for countering unauthorized image manipulation, safeguarding authenticity in the era of advanced generative models. The system was developed by researchers from MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL).

With the possibility of deceiving deep learning models by appropriately modifying images verified, lots of researches on adversarial attacks and adversarial defenses have been carried out in academia. However, there is few research on adversarial attacks and adversarial defenses of point cloud semantic segmentation models, especially in the field of autonomous driving. The stability and robustness of point cloud semantic segmentation models are our primary concerns in this paper. Aiming at the p...

AI-based CryptoGPT is an exciting and promising new approach to cryptography that has the potential to revolutionize the way we secure data and communications.

This paper investigates a new challenging problem called defensive few-shot learning in order to learn a robust few-shot model against adversarial attacks. Simply applying the existing adversarial defense methods to few-shot learning cannot effectively solve this problem. This is because the commonly assumed sample-level distribution consistency between the training and test sets can no longer be met in the few-shot setting. To address this situation, we develop a general defensive few-shot lear...

Caption: A deep-learning algorithm developed by MIT researchers is designed to help machines navigate in the real world. In tests with the video game Pong, the researchers introduced an “adversary” that pulled the ball slightly further down than it actually was. Credits: Image: Jose-Luis Olivares, MIT *Terms of Use: Images for download on the MIT News office website are made available to non-commercial entities,...