Competition on "Evolutionary Computation in MultiLabel Adversarial Examples" at the 2026 IEEE World Congress on Computational Intelligence (IEEE WCCI 2026)

June 21 - June 26, 2026, MECC Maastricht, the Netherlands

Overview

Artificial intelligence (AI), represented by deep learning, has deeply penetrated into multiple fields including production, life, and scientific research, and has been one of the core technological engines that drives the progress of human society. AI replaces repetitive labor through automation, and breaks through the boundaries of human cognition.

However, much work has demonstrated that AI models are faced with some serious security and privacy threats, such as adversarial examples. Adversarial examples—carefully crafted inputs that mislead AI systems—pose significant threats to the reliability and safety of these models. By investigating the adversarial examples and developing efficient attack testing methodologies, we can not only assess the security of models, but also enhance their intrinsic trustworthiness.

The generation of adversarial examples can be formulated as a large-scale optimization problem, for which evolutionary algorithms have shown remarkable effectiveness. This competition focused on a challenging category of adversarial examples—multi-label adversarial examples targeting deep neural networks—and explored the effectiveness of evolutionary computation for black-box multi-label adversarial attacks.

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