AI Technology & Industry Review
A research team from the University of Science and Technology of China, Microsoft Cloud AI, City University of Hong Kong and Wormpex AI Research propose a robust and invisible backdoor attack called “Poison Ink” and demonstrates its immunity to state-of-the-art defence techniques.
A research team from Zhejiang University, Wuhan University and Adobe Research proposes Feature Importance-Aware Attacks (FIA) that drastically improve the transferability of adversarial examples, achieving superior performance compared to state-of-the-art transferable attacks.
An IBM research team proposes four multilingual adversarial attack strategies and attacks seven languages in a zero-shot setting on large multilingual pretrained language models (e.g. MBERT), reducing average performance by up to 85.6 percent.
Researchers introduce the notion of deflecting adversarial attacks, which presents a step towards ending the battle between attacks and defenses.
Researchers from Facebook AI and the University of Maryland have introduced a 21st century version — sweatshirts printed with adversarial examples that make the wearer undetectable to the AI-powered object detectors in today’s public surveillance systems.
Researchers from Lomonosov Moscow State University and Huawei Moscow Research Center have introduced a wearable card designed to perform the opposite function — concealing a person’s identity from facial recognition systems.
Researchers have proposed a new adversarial attack approach, SemanticAdv, which generates adversarial perturbations by manipulating not pixels but rather an image’s semantic attributes.