Image manipulation on the latent space of the pre-trained StyleGAN can control the semantic attributes of the generated images. Recently, some studies have focused on detecting channels with specific properties to dir...
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Online continual learning (OCL), which enables AI systems to adaptively learn from non-stationary data streams, is commonly achieved using experience replay (ER)-based methods that retain knowledge by replaying stored...
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The inability of a computer to think has been a limiter in its usefulness and a point of reassurance for humanity since the first computers were created. The semantic web is the first step toward removing that barrier...
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Multiple Birth Support Vector Machine (MBSVM) is widely used in various engineering fields due to its fast learning efficiency. However, MBSVM does not consider the prior structure information of samples when construc...
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In this paper, a mathematical model is established for the three-dimensional packing problem. This model is a multi-objective optimization problem which considers two factors: volume utilization ratio and load utiliza...
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作者：
Jingyuan XuWeiwei LiuSchool of Computer Science
Wuhan University and National Engineering Research Center for Multimedia Software Wuhan University and Institute of Artificial Intelligence Wuhan University and Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan University
This paper considers the following question: Given the number of classes m, the number of robust accuracy queries k, and the number of test examples in the dataset n, how much can adaptive algorithms robustly overfit ...
This paper considers the following question: Given the number of classes m, the number of robust accuracy queries k, and the number of test examples in the dataset n, how much can adaptive algorithms robustly overfit the test dataset? We solve this problem by equivalently giving near-matching upper and lower bounds of the robust overfitting bias in multiclass classification problems.
作者：
Yanbo ChenWeiwei LiuSchool of Computer Science
Wuhan University and National Engineering Research Center for Multimedia Software Wuhan University and Institute of Artificial Intelligence Wuhan University and Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan University
Transfer-based attacks [1] are a practical method of black-box adversarial attacks in which the attacker aims to craft adversarial examples from a source model that is transferable to the target model. Many empirical ...
Transfer-based attacks [1] are a practical method of black-box adversarial attacks in which the attacker aims to craft adversarial examples from a source model that is transferable to the target model. Many empirical works [2-6] have tried to explain the transferability of adversarial examples from different angles. However, these works only provide ad hoc explanations without quantitative analyses. The theory behind transfer-based attacks remains a *** paper studies transfer-based attacks under a unified theoretical framework. We propose an explanatory model, called the manifold attack model, that formalizes popular beliefs and explains the existing empirical results. Our model explains why adversarial examples are transferable even when the source model is inaccurate as observed in Papernot et al. [7]. Moreover, our model implies that the existence of transferable adversarial examples depends on the "curvature" of the data manifold, which further explains why the success rates of transfer-based attacks are hard to improve. We also discuss our model's expressive power and applicability.
Image fusion combines the complementary traits of source images into a single output, enhancing both human visual observation and machine vision perception. The existing fusion algorithms typically prioritize visual e...
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ISBN:
(数字)9798350354690
ISBN:
(纸本)9798350354706
Image fusion combines the complementary traits of source images into a single output, enhancing both human visual observation and machine vision perception. The existing fusion algorithms typically prioritize visual enhancement, often overlooking the real-time needs for critical surveillance applications. To address these real-time deployment needs, we present a compact fusion network for combining infrared and visible image representations, named Light-weight Fusion (LightFusion). This network employs incremental semantic integration and scene recognition accuracy constraints by incorporating three different bands of images (IR, RGB, and Grayscale) to fuse the data. Our approach includes a sparse semantic perception branch that captures critical semantic features, which are then integrated into the fusion network through a semantic injection module. This ensures that high-level vision tasks are adequately addressed. The scene fidelity path ensures that fusion features preserve all details required to reconstruct the original images. The importance and applicability of the proposed network are enhanced by employing an extra input in the form of a grayscale image, obtained by converting the RGB image for improved contrast, along with prominent target masks to enhance the visual quality of the fusion results. Our extensive analysis shows that the lightweight LightFusion network outperforms existing methods in both visual quality and semantic integrity, even under challenging conditions. The source code will be released at https://***/MI-HussainiLightFusion.
Liveness detection is a part of living biometric identification. While the face recognition system is promoted, it is also vulnerable to deceived and attacked from fake faces. Face liveness detection in traditional me...
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Fog computing enhances traditional cloud computing by bringing resources closer to edge devices, enabling faster response times and supporting mission-critical applications. However, its distributed nature makes it vu...
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