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    Area of Science:

    • Computer Vision
    • Machine Learning
    • Artificial Intelligence

    Background:

    • Convolutional Neural Networks (CNNs) achieve high performance in visual recognition.
    • Adversarial Examples (AEs) are inputs crafted to cause incorrect predictions in CNNs.
    • Robust AEs can evade detection through standard image transformations.

    Purpose of the Study:

    • To explore the detection of AEs using image transformations.
    • To propose a novel defense perturbation method for detecting robust AEs.
    • To introduce and analyze multi-network AEs.

    Main Methods:

    • Investigating image transformations for AE detection.
    • Developing and applying a defense perturbation technique.
    • Generating and evaluating multi-network adversarial examples.

    Main Results:

    • The proposed defense perturbation effectively detects robust AEs.
    • Image transformations are explored as a detection mechanism.
    • Multi-network AEs are demonstrated to fool multiple CNNs simultaneously.

    Conclusions:

    • Defense perturbation offers an effective counter-measure against robust AEs.
    • Multi-network AEs pose a significant threat to redundant systems.
    • Further research into robust AE detection and defense is warranted.