EvoSeed: Unveiling the Threat on Deep Neural Networks with Real-World
  Illusions
Shashank Kotyan PoYuan Mao Danilo Vasconcellos Vargas
Deep neural networks are exploited using natural adversarial samples, which
have no impact on human perception but are misclassified. Current approaches
often rely on the white-box nature of deep neural networks to generate these
adversarial samples or alter the distribution of adversarial samples compared
to training distribution. To alleviate the limitations of current approaches,
we propose EvoSeed, a novel evolutionary strategy-based search algorithmic
framework to generate natural adversarial samples. Our EvoSeed framework uses
auxiliary Diffusion and Classifier models to operate in a model-agnostic
black-box setting. We employ CMA-ES to optimize the search for an adversarial
seed vector, which, when processed by the Conditional Diffusion Model, results
in an unrestricted natural adversarial sample misclassified by the Classifier
Model. Experiments show that generated adversarial images are of high image
quality and are transferable to different classifiers. Our approach
demonstrates promise in enhancing the quality of adversarial samples using
evolutionary algorithms. We hope our research opens new avenues to enhance the
robustness of deep neural networks in real-world scenarios. Project Website can
be accessed at \url{https://shashankkotyan.github.io/EvoSeed}.