AN EFFECTIVE STRATEGY FOR IDENTIFY HIGH QUALITY JPEG COMPRESSION BY USING NETWORKS PREDICTOR IMPLEMENTATION

Abstract

Revealing the Trace of High-Quality JPEG Compression through Quantization Noise Analysis To recognize whether a picture continues to be JPEG compressed is a vital issue in forensic practice. The condition-of-the-art techniques neglect to identify high-quality compressed images that are common on the web. Within this paper, we offer a manuscript quantization noise-based means to fix reveal the traces of JPEG compression. In line with the analysis of noises in multiple-cycle JPEG compression, we define a sum known as forward quantization noise. We analytically derive that the decompressed JPEG image includes a lower variance of forward quantization noise than its uncompressed counterpart. Using the conclusion, we create a simple yet extremely effective recognition formula to recognize decompressed JPEG images. Within this paper, we concentrate on the problem of determining whether a picture presently in uncompressed form is really uncompressed or continues to be formerly JPEG compressed. We analytically derive that the decompressed JPEG image includes a lower variance of forward quantization noise than its uncompressed counterpart. To recognize whether a picture has been JPEG compressed is a vital issue in forensic practice. The suggested formula does apply in certain practical programs, for example Internet image classification and forgery recognition. This Tate-of-the-art techniques neglect to identify high-quality compressed images, that are common on the web. Within this paper, we offer a manuscript quantization noise-based means to fix reveal the traces of JPEG compression. In line with the analysis of noises in multiple-cycle JPEG compression, we define a quantity called forward quantization noise. With the conclusion, we create a simple yet extremely effective detection algorithm to recognize decompressed JPEG images. We show that our method outperforms the condition-of-the-art techniques with a large margin specifically for high-quality compressed images through extensive experiments on various causes of images. We also demonstrate the suggested technique is robust to small image size and chromo sub sampling.