The existing face forgery algorithms have achieved remarkable progress in how to generate reasonable facial images and can even successfully deceive human beings. Considering public security, face forgery detection is of vital importance, making it essential to design face forgery detection algorithms to detect forgery images over the Internet. Despite the great success achieved by the existing Deepfake detection algorithms, they usually failed to achieve satisfactory Deepfake detection performance when deployed to handle the forgery videos in practice. One significant reason is compression. The videos over the Internet are inevitably compressed considering the transmission efficiency. To address this issue, in this paper, we propose a generic, simple yet effective “bleaching” pre-processing module based on the generative model and the high-level feature representations to produce ableached image, which shares a similar appearance with the compressed images. The bleached images with recovered information can be identified accurately by the optimized Deepfake detection models without retraining. The proposed method has utilized a redesigned feature representation, which serves as a navigator to effectively and sufficiently alter the feature distribution in the high-dimensional space to remedy the difference between real facial images and forgery counterparts. Thus, the proposed method can successfully avoid misclassification. Comprehensive and extensive experiments are carried out on four low-quality Faceforensics++ datasets, demonstrating the effectiveness of our method in recovering the information loss caused by the compression artifacts across various backbones and compression.

In order to solve the problem of blurred license plate image in road monitoring, the PSF estimation method based on spectrum is often used to restore the blurred license plate image. Due to the large error of motion blur information estimation, a blind license plate restoration algorithm based on image spectrum and cepstrum is proposed in this paper. First, Radon transform is used to detect the parallel line features in the spectrum of fuzzy license plate image to estimate the fuzzy angle. Then, the cepstrum data of the blurred image is used to measure the peak value and estimate the fuzzy length of the image. Finally, blind restoration of fuzzy license plate image is carried out by Wiener filter of autocorrelation function. The experimental results show that the PSF estimation result of this algorithm is more accurate, and it can achieve better restoration effect of fuzzy image for license plate images under different fuzzy conditions. It is verified that the blind restoration algorithm based on fusion of image spectrum and cepstrum has certain feasibility and adaptability.