Multimodal Backdoor

Multimodal backdoor attacks exploit the vulnerabilities of machine learning models that process multiple data types (e.g., image and text) by injecting malicious triggers into the training data, causing the model to produce incorrect outputs under specific conditions. Current research focuses on developing sophisticated attack methods, such as those employing generative models to create imperceptible triggers or leveraging emotional cues in speech, and designing robust defenses, including adversarial training and techniques to identify and neutralize poisoned features or neurons. Understanding and mitigating these attacks is crucial for ensuring the security and reliability of increasingly prevalent multimodal AI systems across various applications, from visual question answering to speech recognition.