This work provides the community with a timely comprehensive review of backdoor attacks
and countermeasures on deep learning. According to the attacker's capability and affected …

Machine learning (ML) systems are rapidly increasing in size, are acquiring new
capabilities, and are increasingly deployed in high-stakes settings. As with other powerful …

Backdoor learning is an emerging and vital topic for studying deep neural networks'
vulnerability (DNNs). Many pioneering backdoor attack and defense methods are being …

Although backdoor learning is an active research topic in the NLP domain, the literature
lacks studies that systematically categorize and summarize backdoor attacks and defenses …

Humans are capable of strategically deceptive behavior: behaving helpfully in most
situations, but then behaving very differently in order to pursue alternative objectives when …

The backdoor or Trojan attack is a severe threat to deep neural networks (DNNs).
Researchers find that DNNs trained on benign data and settings can also learn backdoor …

The success of deep learning has enabled advances in multimodal tasks that require non-
trivial fusion of multiple input domains. Although multimodal models have shown potential in …

We propose a provable defense mechanism against backdoor policies in reinforcement
learning under subspace trigger assumption. A backdoor policy is a security threat where an …

Deep neural networks have been shown to be vulnerable to backdoor, or Trojan, attacks
where an adversary has embedded a trigger in the network at training time such that the …

As unmanned aerial vehicles (UAVs) become increasingly integrated across various
domains, both military and civilian, safeguarding the security of their navigation systems …