In image security, the robustness of classification models against adversarial attacks is essential. This study explores a fine-grained defense approach, using pixel perturbation (pixel deflection, PD) to disrupt adversarial attempts. We apply PD across various image types and attacks, without depending on a specific dataset or revealing implementation details. Results show PD can partially mitigate attacks, but success depends on parameters like deflection count, neighborhood size, and image characteristics. High-texture images demonstrate stronger resilience than simpler ones. This systematic evaluation highlights PD’s conditional effectiveness and the need to tailor defenses to specific attacks and image properties.