Yazar "Wu, Tao" seçeneğine göre listele

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    Aliasing black box adversarial attack with joint self-attention distribution and confidence probability
    (Pergamon-Elsevier Science Ltd, 2023) Liu, Jun; Jin, Haoyu; Xu, Guangxia; Lin, Mingwei; Wu, Tao; Polat, Kemal
    Deep neural networks (DNNs) are vulnerable to adversarial attacks, in which a small perturbation to samples can cause misclassification. However, how to select important words for textual attack models is a big challenge. Therefore, in this paper, an innovative score-based attack model is proposed to solve the important words se-lection problem for textual attack models. To this end, the generation of semantically adversarial examples in this model is adopted to mislead a text classification model. Then, this model integrates the self-attention mechanism and confidence probabilities for the selection of the important words. Moreover, an alternative model similar to the transfer attack is introduced to reflect the correlation degree of words inside the texts. Finally, adversarial training experimental results demonstrate the superiority of the proposed model.
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    Fusion of overexposed and underexposed images using caputo differential operator for resolution and texture based enhancement
    (Springer, 2023) Zhou, Liang; Alenezi, Fayadh S.; Nandal, Amita; Dhaka, Arvind; Wu, Tao; Polat, Kemal
    The visual quality of images captured under sub-optimal lighting conditions, such as over and underexposure may benefit from improvement using fusion-based techniques. This paper presents the Caputo Differential Operator-based image fusion technique for image enhancement. To effect this enhancement, the proposed algorithm first decomposes the overexposed and underexposed images into horizontal and vertical sub-bands using Discrete Wavelet Transform (DWT). The horizontal and vertical sub-bands are then enhanced using Caputo Differential Operator (CDO) and fused by taking the average of the transformed horizontal and vertical fractional derivatives. This work introduces a fractional derivative-based edge and feature enhancement to be used in conjuction with DWT and inverse DWT (IDWT) operations. The proposed algorithm combines the salient features of overexposed and underexposed images and enhances the fused image effectively. We use the fractional derivative-based method because it restores the edge and texture information more efficiently than existing method. In addition, we have introduced a resolution enhancement operator to correct and balance the overexposed and underexposed images, together with the Caputo enhanced fused image we obtain an image with significantly deepened resolution. Finally, we introduce a novel texture enhancing and smoothing operation to yield the final image. We apply subjective and objective evaluations of the proposed algorithm in direct comparison with other existing image fusion methods. Our approach results in aesthetically subjective image enhancement, and objectively measured improvement metrics.
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    Fusion of overexposed and underexposed images using caputo differential operator for resolution and texture based enhancement (nov, 10.1007/s10489-022-04344-z, 2022)
    (Springer, 2023) Zhou, Liang; Alenezi, Fayadh S. S.; Nandal, Amita; Dhaka, Arvind; Wu, Tao; Polat, Kemal
    The article Fusion of overexposed and underexposed images using caputo differential operator for resolution and texture based enhancement, written by Liang Zhou, Fayadh S. Alenezi, Amita Nandal, Arvind Dhaka, Tao Wu, Deepika Koundal, Adi Alhudhaif and Kemal Polat, was originally published electronically on the publisher’s internet portal on November 29, 2022 without open access.