With the flourishing prosperity of generative models, manipulated facial images have become increasingly accessible, raising concerns regarding privacy infringement and societal trust. In response, proactive defense strategies embed adversarial perturbations into facial images to counter deepfake manipulation. However, existing methods often face a tradeoff between imperceptibility and defense effectiveness-strong perturbations may disrupt forgeries but degrade visual fidelity. Recent studies have attempted to address this issue by introducing additional visual loss constraints, yet often overlook the underlying gradient conflicts among losses, ultimately weakening defense performance. To bridge the gap, we propose a gradient-projection-based adversarial proactive defense (GRASP) method that effectively counters facial deepfakes while minimizing perceptual degradation. GRASP is the first approach to successfully integrate both structural similarity loss and low-frequency loss to enhance perturbation imperceptibility. By analyzing gradient conflicts between defense effectiveness loss and visual quality losses, GRASP pioneers the design of the gradient-projection mechanism to mitigate these conflicts, enabling balanced optimization that preserves image fidelity without sacrificing defensive performance. Extensive experiments validate the efficacy of GRASP, achieving a PSNR exceeding 40 dB, SSIM of 0.99, and a 100% defense success rate against facial attribute manipulations, significantly outperforming existing approaches in visual quality.