This paper addresses the critical challenge of detecting AI-generated images produced by text-to-image models. The proposed method is training-free and measures representation sensitivity to structured frequency perturbations, enabling detection of subtle manipulations between real and synthetic images. The approach uses only a single Fourier transform for perturbation generation, making it computationally lightweight and achieving one to two orders of magnitude faster inference than existing training-free detectors. Extensive experiments on challenging benchmarks including the OpenFake benchmark demonstrate superior performance over state-of-the-art methods.