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.