Anti-forensics of Decompressed Bitmap Based on Generative Adversarial Network
In view of the traces of chroma upsampling introduced during the image decompression process,which lead to different distributions of differences between odd-even and even-odd pixel pairs on the chroma plane of uncompressed and decompressed images,a decompressed bitmap anti-forensics method based on generative ad-versarial networks is proposed.The approach models JPEG decompression anti-forensic work as image-to-image conversion.On this basis,a loss function is designed for the chroma upsampling traces introduced during the de-compression process.After iterative training,the model can generate reconstructed images with extremely high vis-ual quality and reasonable statistical characteristics.Experimental results show that the modified images generated by this anti-forensics method are able to deceive existing detectors and have excellent visual quality.In terms of objective evaluation indicators,this anti-forensics method has achieved remarkable results in reducing the accura-cy of the detector.Compared with other anti-forensics methods based on generative adversarial networks,when the compression quality is 50,the peak signal-to-noise ratio and structural similarity of the generated images have shown a certain improvement.