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Existing High Efficiency Video Coding (HEVC) selective encryption algorithms only consider the encoding characteristics of syntax elements to keep format compliance, but ignore the semantic features of video content, which may lead to unnecessary computational and bit rate costs. To tackle this problem, we present a content-aware tunable selective encryption (CATSE) scheme for HEVC. First, a deep hashing network is adopted to retrieve groups of pictures (GOPs) containing sensitive objects.
Image set compression (ISC) refers to compressing the sets of semantically similar images. Traditional ISC methods typically aim to eliminate redundancy among images at either signal or frequency domain, but often struggle to handle complex geometric deformations across different images effectively.
Explanatory Visual Question Answering (EVQA) is a recently proposed multimodal reasoning task consisting of answering the visual question and generating multimodal explanations for the reasoning processes. Unlike traditional Visual Question Answering (VQA) task that only aims at predicting answers for visual questions, EVQA also aims to generate user-friendly explanations to improve the explainability and credibility of reasoning models.
Existing JPEG encryption approaches pose a security risk due to the difficulty in changing all block-feature values while considering format compatibility and file size expansion. To address these concerns, this paper introduces a novel JPEG image encryption scheme. First, the security of sketch information against chosen-plaintext attacks is improved by increasing the change rate of block-feature values.
Existing JPEG encryption approaches pose a security risk due to the difficulty in changing all block-feature values while considering format compatibility and file size expansion. To address these concerns, this paper introduces a novel JPEG image encryption scheme. First, the security of sketch information against chosen-plaintext attacks is improved by increasing the change rate of block-feature values.
Deep neural networks have demonstrated considerable effectiveness in recognizing complex communications signals through their applications in the tasks of automatic modulation recognition. However, the resilience of these networks is undermined by the introduction of carefully designed adversarial examples that compromise the reliability of the decision processes.
Date: 30 January 2025 (Virtual)
Chapter: Gujarat Chapter
Chapter Chair: Mita C. Paunwala
Title: Brain-Computer Interfaces Applications and Challanges
Date: 6-10 July 2026
Location: Bangkok, Thailand
Paper Submission Deadline: Coming soon
Website: Coming soon
Date: 22-24 September 2026
Location: Istanbul, Turkey
Paper Submission Deadline: Coming soon
Website: Coming soon
Luleå University of Technology is in strong growth with world-leading competence in several research areas. We shape the future through innovative education and ground-breaking research results, and based on the Arctic region, we create global social benefit. Our scientific and artistic research and education are conducted in close collaboration with international, national and regional companies, public actors and leading universities.
Date: 31 January 2025
Time: 1:00 PM ET (New York Time)
Presenter(s): Dr. Sepideh Sadaghiani
Date: 26 June 2025
Chapter: Greece
Chapter Chair: Christophoros Nikou
Title: Matrix and Tensor Factorizations for Neuroimaging Data Analysis and Fusion
Date: 25-27 June 2025
Location: Costa Navarino, Messinia, Greece
Context
Date: 21 January 2025
Time: 10:00 AM ET (New York time)
Presenter(s): Boris Ivanovic