Improving Transferability of Adversarial Attacks via Maximization and Targeting from Image to Video Quality Assessment

This paper proposes a novel method for transferable adversarial attacks from Image Quality Assessment (IQA) to Video Quality Assessment (VQA) models. Attacking modern VQA models is challenging due to their high complexity and the temporal nature of video content. Since IQA and VQA models share similar low- and mid-level feature representations, and IQA models are substantially cheaper and faster to run, we leverage them as surrogates to generate transferable adversarial perturbations. Our method, MaxT-I2VQA jointly Maximizes IQA scores and Targets IQA feature activations to improve transferability from IQA to VQA models. We first analyze the correlation between IQA and VQA internal features and use these insights to design a feature-targeting loss. We evaluate MaxT-I2VQA by transferring attacks from four state-of-the-art IQA models to four recent VQA models and compare against three competitive baselines. Compared to prior methods, MaxT-I2VQA increases the transferability of an attack success rate by 7.9% and reduces per-example attack runtime by 8 times. Our experiments confirm that IQA and VQA feature spaces are sufficiently aligned to enable effective cross-task transfer.