While machine learning (ML) models have achieved great success in many perception applications, concerns have risen about their potential security, robustness, privacy, and transparency issues when applied to real-world applications. Irresponsibly applying a foundation model to mission-critical and human-centric domains can lead to serious misuse, inequity issues, negative economic and environmental impacts, and/or legal and ethical concerns. For example, ML models are often regarded as “black boxes” and can produce unreliable, unpredictable, and unexplainable outcomes, especially under domain shifts or maliciously crafted attacks, challenging the reliability of safety-critical applications; Stable Diffusion may generate NSFW content and privacy violated-content.
This goals of this tutorial are to:
Provide a holistic and complementary overview of trustworthiness issues, including security, robustness, privacy, and societal issues to allow a fresh perspective and some reflection on the induced impacts and responsibility as well as introduce the potential solutions.
Promote awareness of the misuse and potential risks in existing AI techniques and, more importantly, to motivate rethinking of trustworthiness in research.
Present case studies from computer vision-based applications.
This tutorial will provide sufficient background for participants to understand the motivation, research progress, known issues, and ongoing challenges in trustworthy perception systems, in addition to pointers to open-source libraries and surveys.