Text-embedded images are frequently used on social media to convey opinions and emotions, but they can also be a medium for disseminating hate speech, propaganda, and extremist ideologies. During the Russia-Ukraine wa...
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As deep learning models increasingly find applications in critical domains such as medical imaging, the need for transparent and trustworthy decision-making becomes paramount. Many explainability methods provide insig...
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Cancelable biometrics refers to a group of techniques in which the biometric inputs are transformed intentionally using a key before processing or storage. This transformation is repeatable enabling subsequent biometr...
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ISBN:
(纸本)9781665458245
Cancelable biometrics refers to a group of techniques in which the biometric inputs are transformed intentionally using a key before processing or storage. This transformation is repeatable enabling subsequent biometric comparisons. This paper introduces a new scheme for cancelable biometrics aimed at protecting the templates against potential attacks, applicable to any biometric-based recognition system. Our proposed scheme is based on timevarying keys obtained from morphing random biometric information. An experimental implementation of the proposed scheme is given for face biometrics. The results confirm that the proposed approach is able to withstand against leakage attacks while improving the recognition performance.
Due to the recent progress in Deepfake generation, several datasets and manipulation techniques have been proposed in the recent literature with various effective face-swap and face-reenactment methods. Deepfake is an...
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ISBN:
(纸本)9798350394948;9798350394955
Due to the recent progress in Deepfake generation, several datasets and manipulation techniques have been proposed in the recent literature with various effective face-swap and face-reenactment methods. Deepfake is an emerging threat to society and government as it can jeopardize law enforcement and cause personal loss. Investigations in the literature established that demographic variation had impacted the performance of Deepfake detection. To date, Deepfake detection has not been studied in the Indian context;hence, in this work, we proposed a Deepfake dataset INDIFACE entirely with Indian subjects. We have collected 101 original videos and used two different manipulation techniques for Deepfake generation. We provide detailed benchmarking with state-of-the-art methods on Deepfake datasets, showcasing that the existing model is insufficient to detect Deepfake detection for the Indian scenario. Hence, more attention is required to this area of research. The proposed dataset INDIFACE is publicly available at
This paper presents the first challenge on demosaicing of natural spectral images for snapshot hyperspectral imaging systems (HIS) which utilize a multi-spectral filer array (MSFA), i.e., the recovery of whole-scene h...
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ISBN:
(数字)9781665487399
ISBN:
(纸本)9781665487399
This paper presents the first challenge on demosaicing of natural spectral images for snapshot hyperspectral imaging systems (HIS) which utilize a multi-spectral filer array (MSFA), i.e., the recovery of whole-scene hyperspectral information from spatially sub-sampled hyperspectral information. This challenge expands the "ARAD_1K" data set to a first-of-its-kind large-scale data set for multi-spectral filter array demosaicing of natural scenes containing 1,000 images. Challenge participants were required to recover hyperspectral information from synthetically generated MSFA images simulating capture by a known calibrated snapshot mosaic hyperspectral camera. The challenge was attended by 157 teams, with 29 teams competing in the final testing phase, 7 of which provided detailed descriptions of their methodology which are included in this report. The performance of these submissions is reviewed and provided here as a gauge for the current state-of-the-art in multi-spectral filter array demosaicing of natural images.
Recent years have witnessed an increased interest in image dehazing. Many deep learning methods have been proposed to tackle this challenge, and have made significant accomplishments dealing with homogeneous haze. How...
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Facial micro-expressions (MEs) refer to subtle, transient, and involuntary muscle movements expressing a person's true feelings. This paper presents a novel two-stream relational edge-node graph attention network-...
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Anomaly detection is a classical problem within automated visual surveillance, namely the determination of the normal from the abnormal when operational data availability is highly biased towards one class (normal) du...
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To achieve autonomous driving, developing 3D detection fusion methods, which aim to fuse the camera and LiDAR information, has draw great research interest in recent years. As a common practice, people rely on large-s...
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Image dehazing is one of the most challenging imaging inverse problems that estimates the haze-free images from hazy ones. While recent transformer/convolutional neural network-based methods have shown excellent perfo...
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