The widespread use of face-retouching techniques across social media platforms has generated significant societal concerns that extend beyond aesthetics，giving rise to psychological consequences，such as body dysmorphic disorder，undermining trust in digital imagery and reinforcing pervasive cultural norms surrounding beauty standards. Face-retouching is widely perceived as more convenient than face swapping and has been integrated into numerous social network applications. Although existing research has made substantial progress in detecting retouched faces using a range of computational approaches，the critical task of reversing these modifications to reconstruct original facial features and restore authenticity in digital images remains largely unexplored. This disparity highlights a significant gap between detection capabilities and the development of practical restoration solutions. This study formally introduces face retouching restoration （FRR） as a novel computer vision task，distinguished from related problems，such as makeup restoration and general image restoration，by its explicit focus on subtle textural modifications and extreme structural alterations characteristic of retouched facial images. The primary objective is to accurately recover authentic facial characteristics from heavily manipulated images by addressing two key technical challenges： restoring fine-grained textures，such as skin smoothing，and reconstructing facial geometry，including eye enlargement and jaw reshaping. This dual focus on texture and structure advances beyond existing methods that typically address only a single aspect of facial modification. Furthermore，this study aims to establish standardized evaluation protocols and datasets for this emerging research direction，recognizing that existing benchmarks designed for retouching detection are inadequate for assessing restoration quality. The proposed approach advances digital media forensics by providing practical tools for content authentication while simultaneously contributing to the broad discussions on image manipulation in the digital age. This study aims to shift from passive detection to active restoration，with potential applications in forensic analysis，psychological counseling，and digital media literacy initiatives.

The proposed retouching-guided diffusion model for FRR （RT-FRR） introduces a novel dual-component architecture that synergistically integrates diffusion-based generation modeling with targeted retouching artifact analysis. The framework incorporates a face retouching estimation module （FREM） that utilizes constrained BayarConv layers with 5 × 5 × 3 kernels，specifically designed to amplify manipulation artifacts while suppressing authentic facial features. This preprocessing stage extracts multi-scale retouching traces through four successive ResNet blocks，capturing local pixel-level inconsistencies and global structural alterations. These hierarchical features serve as conditional guidance for the second component—a denoising diffusion probabilistic model configured with 1 000 timesteps that learns to predict the precise residual between retouched and original facial images. The model’s U-Net backbone incorporates innovative cross-attention mechanisms at four distinct network depths，allowing progressive integration of retouching traces with diffusion priors throughout the denoising process. This multi-scale attention strategy facilitates simultaneous recovery of high-frequency textural details，such as skin pores and fine wrinkles，and macroscopic structural features，including facial contours and organ geometry. Training optimization utilizes the Adam algorithm across 100 000 iterations with a fixed learning rate of 10^-4 using a composite loss function that balances pixel-wise reconstruction accuracy with perceptual quality metrics. The authors created the extreme retouched face dataset （ExRF） comprising 1 000 high-resolution image pairs with severe yet plausible retouching operations，manually generated using professional editing software with consistent extreme parameter settings，for a comprehensive evaluation. This dataset complements the existing RetouchingFFHQ benchmark，ensuring robust validation across the full spectrum of retouching intensities from subtle enhancements to dramatic transformations. The framework operates on 512 × 512 resolution images，maintaining architectural consistency with state-of-the-art diffusion models while being specifically optimized for facial feature recovery tasks.

Experimental evaluation demonstrates that RT-FRR achieves state-of-the-art performance in face retouching restoration，significantly outperforming existing methods across multiple quantitative and qualitative metrics. On the ExRF dataset，which features significantly retouched faces，RT-FRR achieves a PSNR of 25.23 dB，surpassing the second-best method ResDiff by 1.9 dB，along with superior scores in SSIM at 0.806，FID at 48.802，and LPIPS at 0.241. These results highlight the model’s effectiveness in recovering subtle textural details and major structural modifications，such as eye enlargement，nose reshaping，and facial contour adjustments. Comparative analysis against nine baseline methods，including makeup removal techniques，such as CSD-MIT and BeautyGAN，as well as general image restoration approaches，such as Restormer and SR3，demonstrates that RT-FRR consistently delivers high-fidelity reconstructions，particularly in cases of significant retouching where prior methods frequently fail. On the RetouchingFFHQ dataset，which features mild retouching，RT-FRR maintains strong performance with a PSNR of 25.39 dB，demonstrating robustness across different retouching intensities. Qualitative assessments reinforce these findings，with feature similarity heatmaps and t-SNE visualizations confirming that RT-FRR more accurately recovers facial identity characteristics than competing approaches. The model successfully reconstructs fine-grained skin textures while correcting exaggerated structural changes，such as unnaturally thinned jawlines or enlarged eyes，without introducing artifacts common in GAN-based or transformer-based methods. Ablation studies underscore the importance of key design choices，where the face residual generation strategy alone contributes a 4 dB PSNR improvement，while the FREM module adds an additional 2 dB by localizing retouching traces. Visual comparisons further demonstrate RT-FRR’s ability to restore natural facial proportions and skin details，even in cases where retouching has heavily distorted the original appearance. These results establish RT-FRR as a comprehensive solution for face retouching restoration，effectively addressing textural and structural recovery with unprecedented accuracy.

In this study，we focus on FRR，a novel task focused on recovering original facial features from retouched ones，particularly those that are significantly retouched. We propose RT-FRR to recover the textural and structural face features from a retouched face，where a face retouching estimation module is designed and incorporated to extract the retouching trace to guide the FRR. The retouching traces are integrated with the denoising U-Net of the DDPM through an attention mechanism for an accurate face residual generation. We evaluate the performance of RT-FRR on an existing face retouching dataset and the newly constructed ExRF. On both datasets，our RF-FRR outperforms existing makeup removal and image restoration schemes in the task of FRR.