10 accepted papers
Recent advancements in diffusion-based image editing pose a significant threat to the authenticity of digital visual content. Traditional embedding-based watermarking methods often introduce perceptible perturbations to maintain robustness, inevitably compromising visual fidelity. Meanwhile, existin
Reliable watermarking of panoramic imagery is fundamentally challenged by arbitrary 3D rotations. As panoramas are defined on the sphere, they naturally transform under the action of $SO(3)$, rendering conventional planar representations and augmentation-based robustness strategies inadequate and de…
Accurate stereo matching under fast motion and extreme lighting conditions is a challenge for many vision applications. Event cameras have the advantages of low latency and high dynamic range, thus providing a reliable solution to this challenge. However, since events are sparse, this makes it an il…
For general users, training a neural network from scratch is usually challenging and labor-intensive. Fortunately, neural network zoos enable them to find a well-performing model for directly use or fine-tuning it in their local environments. Although current model retrieval solutions attempt to con…
Diffusion models have significantly advanced the field of image synthesis, making the protection of their intellectual property (IP) a critical concern. Existing IP protection methods primarily focus on embedding watermarks into generated images by altering the structure of the diffusion process. Ho…
Large Language Models (LLMs) are prone to hallucinations, which pose significant risks in their applications. Most existing hallucination detection methods rely on internal probabilities or external knowledge, and they are limited to identifying hallucinations at the sentence or passage level. In th…
Large Vision-Language Models (LVLMs) are gradually becoming the foundation for many artificial intelligence applications. However, understanding their internal working mechanisms has continued to puzzle researchers, which in turn limits the further enhancement of their capabilities. In this paper, w…
We present a novel approach for detecting soldering defects in Printed Circuit Boards (PCBs) composed mainly of Surface Mount Technology (SMT) components, using advanced computer vision and deep learning techniques. The main challenge addressed is the detection of soldering defects in new components…
The application of deep neural networks (DNNs) on 360-degree images has achieved remarkable progress in the recent years. However, DNNs have been demonstrated to be vulnerable to well-crafted adversarial examples, which may trigger severe safety problems in the real-world applications based on 360-d…
The safety of Deep Neural Networks (DNNs) processing omnidirectional images (ODIs) is an under-researched topic. In this paper, we propose a novel sparse attack, named Single-Perspective (SP) Attack, towards fooling these models by perturbing only one perspective image (PI) rendered from the target…