Adversarial Attack with Controllable Transferability

Machine Learning as a Service (MLaaS) providers often promote the robustness of their models as a selling point for their API services. Existing methods commonly evaluate the robustness of Deep Neural Networks (DNNs) by generating highly transferable adversarial examples However, dishonest MLaaS providers may deceive consumers by falsely exaggerating the robustness of their models. In this paper, contrary to enhancing the transferability of adversarial examples, we attempt to craft adversarial examples that fail under a shielded model. In other words, ensuring that the adversarial examples fail to attack a shielded model but successfully attack other models, which we refer to as controllable transferability. To achieve this goal, we propose the Controllable Transferability Method (CTM), a framework that generates adversarial examples with controllable transferability. CTM involves generating transferable adversarial examples and refining their transferability using gradient antagonism. Experimental results demonstrate that CTM achieves high transferability across models, with controlled adversarial effects on selected models.