Head Feature-Based Adversarial Attack for Proactive Defense Against Face Swapping

Face swapping, a deepfake technique that composites the face of a source individual onto the image of a target individual, has emerged as a significant security threat that needs to be addressed. Our study aims to proactively prevent images from being exploited in face swapping applications by injecting imperceptible perturbation into the images. To achieve this, a novel perturbation injection method that effectively maximizes the impact of the perturbation is proposed by specifically injecting perturbations into the Head Features. The proposed adversarial attack method is evaluated against existing adversarial attack methods to verify its effectiveness. Additionally, experiments were conducted to evaluate the practical applicability of the proposed method by examining its performance over different inference times and target transferability. The results demonstrated that the proposed method is sufficiently practical in terms of performance for real-world applications.