Recent Advances in Robust Trustworthy Computer Vision
A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Electronic Multimedia".
Deadline for manuscript submissions: closed (15 January 2024) | Viewed by 234
Special Issue Editor
Interests: computer vision; image generation/editing; image understanding; low-level vision; image/multimodal retrieval; AI security; adversarial attack/defense; backdoor attack/defense
Special Issue Information
Dear Colleagues,
Deep learning has achieved significant success in multiple fields, including computer vision. However, studies in adversarial machine learning also indicate that deep learning models are highly vulnerable to adversarial examples. Extensive works have demonstrated that adversarial examples compromise the robustness of deep neural networks, threatening deep-learning-based applications in both the digital and physical world.
For example, although face-recognition algorithms have been broadly adopted in real-world applications, it is still possible to use adversarial images to fool these state-of-the-art systems. Besides image classification, many other tasks such as image retrieval, object detection, and object segmentation have concerns about robustness and trustworthiness.
In this Special Issue, we will focus on the most recent progress and the future directions of adversarial machine learning, especially in computer vision. Our scope ranges from adversarial attacks and backdoor attacks to their corresponding defensive techniques, aiming to explore both the positive and negative aspects for building robust and trustworthy computer vision models.
Prof. Dr. Zhenxing Niu
Guest Editor
Manuscript Submission Information
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Keywords
- adversarial machine learning
- computer vision
- robustness and trustworthiness
- adversarial attack and defense
- backdoor attack and defense
- physical attack
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