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Salt particles that have dissolved in water never spontaneously come back together in solution to reform solid particles. Moreover, a gas that has expanded in a vacuum remains dispersed and never spontaneously reassembles. The unidirectional nature of these phenomena is the result of a thermodynamic state function called entropy (S). Entropy is the measure of the extent to which the energy is dispersed throughout a system, or in other words, it is proportional to the degree of disorder of a...
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A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
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Approaching Adversarial Example Classification with Chaos Theory.

Anibal Pedraza1, Oscar Deniz1, Gloria Bueno1

  • 1VISILAB, University of Castilla La Mancha, 13001 Ciudad Real, Spain.

Entropy (Basel, Switzerland)
|December 8, 2020
PubMed
Summary
This summary is machine-generated.

Chaos theory methods, specifically Lyapunov exponents, can detect adversarial examples in deep learning. Combining these with image entropy significantly enhances detection accuracy against various attacks and image transformations.

Keywords:
Lyapunovadversarial exampleschaos theorydeep learningentropy

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Area of Science:

  • Deep Learning
  • Chaos Theory
  • Image Processing

Background:

  • Adversarial examples, imperceptible input perturbations, pose a significant challenge to robust deep learning models.
  • Current research focuses on developing attack/defense methods and detecting adversarial examples to mitigate risks.

Purpose of the Study:

  • To investigate the application of chaos theory methods for distinguishing adversarial examples from regular images.
  • To assess the robustness of Lyapunov exponents in adversarial example detection and explore complementary methods.

Main Methods:

  • Utilized Lyapunov exponents, a measure of chaoticity, to analyze deep network behavior.
  • Investigated the impact of image processing transformations on Lyapunov exponent robustness.
  • Proposed a complementary approach combining Lyapunov exponents with image entropy for enhanced discrimination.

Main Results:

  • Lyapunov exponents alone are not robust to image processing transformations altering image entropy.
  • Integrating image entropy with Lyapunov exponents significantly improves the accuracy of adversarial example detection.
  • The proposed method achieved 65%-100% accuracy on MNIST, Fashion-MNIST, and CIFAR 19 datasets against diverse attacks and transformations.

Conclusions:

  • Chaos theory, particularly when combined with image entropy, offers a robust approach to detecting adversarial examples.
  • The findings suggest a pathway to enhance classifier robustness against adversarial attacks in real-world and threatening scenarios.