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Accurate signal sampling and reconstruction are crucial in various signal-processing applications. A time-domain signal's spectrum can be revealed using its Fourier transform. When this signal is sampled at a specific frequency, it results in multiple scaled replicas of the original spectrum in the frequency domain. The spacing of these replicas is determined by the sampling frequency.
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Association areas are regions of the cerebral cortex that do not have a specific sensory or motor function. Instead, they integrate and interpret information from various sources to enable higher cognitive processes such as memory, learning, and decision-making. Some key association areas include the following:
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The innovation of touch-tone telephony revolutionized the telecommunications industry by replacing the traditional rotary dial with a dual-tone multi-frequency (DTMF) signaling system. This system uses a matrix-style keypad with buttons arranged in four rows and three columns, creating 12 distinct signals each assigned to a pair of frequencies. Each button press results in a simultaneous generation of two sinusoidal tones – one from a low-frequency group (697 to 941 Hz) and one from a...
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Related Experiment Video

Updated: Aug 30, 2025

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
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Frequency-Tuned Universal Adversarial Attacks on Texture Recognition.

Yingpeng Deng, Lina J Karam

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |September 2, 2022
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    Summary
    This summary is machine-generated.

    Deep neural networks (DNNs) for texture recognition are vulnerable to adversarial attacks. A new frequency-tuned attack method creates less perceptible perturbations, improving defenses and cross-dataset transferability.

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    Area of Science:

    • Computer Vision
    • Machine Learning
    • Artificial Intelligence

    Background:

    • Deep neural networks (DNNs) are susceptible to adversarial attacks in natural image classification.
    • The impact of adversarial attacks on DNN-based texture recognition remains underexplored.
    • Spatial domain perturbation constraints may not effectively limit perceptibility in texture images.

    Purpose of the Study:

    • To investigate the vulnerability of DNN-based texture recognition to adversarial attacks.
    • To propose a novel frequency-tuned universal attack method for texture recognition.
    • To evaluate the effectiveness of the proposed method in terms of perturbation perceptibility and attack success rates.

    Main Methods:

    • Developed a frequency-tuned universal attack method operating in the frequency domain.
    • Computed universal perturbations based on local visual frequency characteristics relevant to human perception.
    • Evaluated the method on various DNN texture classifiers and datasets using white-box fooling rates.

    Main Results:

    • The proposed frequency-tuned attack generates less perceptible perturbations compared to existing methods.
    • Achieved similar or higher white-box fooling rates on DNN texture classifiers.
    • Demonstrated improved attack robustness against defended models and enhanced cross-dataset transferability.

    Conclusions:

    • Limiting perturbations in the spatial domain is insufficient for texture recognition adversarial attacks.
    • The frequency-tuned universal attack is a more effective method for texture recognition.
    • The proposed approach enhances the robustness and transferability of adversarial attacks in texture recognition.