Fixed-budget attacks aim to generate adversarial examples—carefully crafted inputs designed to induce misclassifications during inference—while adhering to a predefined perturbation budget. These attacks maximize misclassification confidence and benefit from the transferability property, enabling the generated adversarial examples to remain effective even against multiple unknown models. However, to preserve their transferability, such attacks often yield perceptible perturbations, compromising the visual integrity of the adversarial examples. In this paper, we introduce HORNET, an extension of gradient-based fixed-budget attacks designed to minimize the perturbation magnitude of adversarial examples while maintaining their transferability against the target model. HORNET utilizes a distinct source model to craft the adversarial examples and employs a limited number of queries to the unknown target model to further minimize perturbation magnitude. We evaluate HORNET empirically by integrating it with 41 existing attack implementations and testing it against 9 different models, resulting in a total of 1700 unique configurations. Our results demonstrate that HORNET outperforms the state of the art in generating minimally perturbed yet highly transferable adversarial examples across all tested models. Code available at: https://github.com/louiswup/HORNET .

Adversarial reprogramming allows repurposing a machine-learning model to perform a different task. For example, a model trained to recognize animals can be reprogrammed to recognize digits by embedding an adversarial program in the digit images provided as input. Recent work has shown that adversarial reprogramming may not only be used to abuse machine-learning models provided as a service, but also beneficially, to improve transfer learning when training data is scarce. However, the factors affecting its success are still largely unexplained. In this work, we develop a first-order linear model of adversarial reprogramming to show that its success inherently depends on the size of the average input gradient, which grows when input gradients are more aligned, and when inputs have higher dimensionality. The results of our experimental analysis, involving fourteen distinct reprogramming tasks, show that the above factors are correlated with the success and the failure of adversarial reprogramming.

Adversarial reprogramming allows stealing computational resources by repurposing machine learning models to perform a different task chosen by the attacker. For example, a model trained to recognize images of animals can be reprogrammed to recognize medical images by embedding an adversarial program in the images provided as inputs. This attack can be perpetrated even if the target model is a black box, supposed that the machine-learning model is provided as a service and the attacker can query the model and collect its outputs. So far, no defense has been demonstrated effective in this scenario. We show for the first time that this attack is detectable using stateful defenses, which store the queries made to the classifier and detect the abnormal cases in which they are similar. Once a malicious query is detected, the account of the user who made it can be blocked. Thus, the attacker must create many accounts to perpetrate the attack. To decrease this number, the attacker could create the adversarial program against a surrogate classifier and then fine-tune it by making a few queries to the target model. In this scenario, the effectiveness of the stateful defense is reduced, but we show that it is still effective.