Co-organized NeurIPS Workshop
NeuIPS workshop on Backdoors in Deep Leaarning: The Good, the Bad and the Ugly - The Good, the Bad and the Ugly - Modern AI development requires using and sharing of models and data safely. Uncovering backdoor, a foe and a friend at the front door.
Deep neural networks (DNNs) are revolutionizing almost all AI domains and have become the core of many modern AI systems. Despite their superior performance compared to classical methods, DNNs also face new security problems, such as adversarial and backdoor attacks, that are hard to discover and resolve due to their black-box-like property. Backdoor attacks are possible because of insecure model pretraining and outsourcing practices. Due to the complexity and the tremendous cost of collecting data and training models, many individuals/companies employ models or training data from third parties. Malicious third parties can add backdoors into their models or poison their released data before delivering it to the victims to gain illegal benefits. This threat seriously damages the safety and trustworthiness of AI development.
While most works consider backdoors “evil”, some studies leverage them for good purposes. A popular approach is to use the backdoor as a watermark to detect illegal uses of commercialized data/models. Watermarks can also be used to mark generated data, which becomes crucial with the introduction of big generative models (LLMs, text-to-image generators). For instance, the paper “A Watermark for Large Language Models” has received an outstanding paper award at ICML 2023, showing the community’s great interest in this critical topic. Besides, a few works employ the backdoor as a trapdoor for adversarial defense. Learning the underlying working mechanisms of backdoors also elevates our understanding of how deep learning models work. This workshop is designed to provide a comprehensive understanding of the current state of backdoor research. Our goal is to foster discussion and perspective exchange, as well as to engage the community in identifying social good applications of backdoors.
Workshop Home Page
https://neurips2023-bugs.github.io/