为探究Snapseed图像处理软件对JPEG图像的Exif信息和原始性的潜在影响,本文以JPEG图像为研究对象,利用Snapseed软件对图像进行处理,通过使用HashMyFiles、MagicExif元数据编辑器、Opanda PowerExif 1.2及WinHex等工具提取图像处理前后的MD5值进行数据完整性校验,同时,利用工具记录图像处理前后的详细Exif信息并检查分析潜在的数据变化。实验结果表明,Snapseed软件导致JPEG图像的Exif信息和原始性发生了根本性的变化,包括部分原始信息被修改或删除、MD5值改变等。因此,在当前Snapseed软件在日常生活中使用频繁的情况下,需要关注其在司法鉴定和数字图像取证等领域中的应用。要确保重要照片原始性和元数据信息不被改变,首先,保留原始文件,不对图像做导入–导出、编辑操作;其次,确认拍摄信息;最后,考虑写入数字签名,以确保照片的原始性不被改变。In this paper, to investigate the potential effects of Snapseed image processing software on Exif information and originality of JPEG images, JPEG images were taken as the research object, and the images were processed using Snapseed software, and the MD5 values before and after image processing were extracted for data integrity checking by using tools such as HashMyFiles, MagicExif metadata editor, Opanda PowerExif 1.2 and WinHex, and at the same time, the tools were used to record the detailed Exif information before and after image processing and check and analyze the potential data changes. The experimental results show that the Snapseed software leads to fundamental changes in the Exif information and originality of JPEG images, including the modification or deletion of some of the original information and changes in the MD5 value. Therefore, in the current situation where Snapseed software is frequently used in daily life, it is necessary to pay attention to its application in the fields of forensics and digital image forensics, etc. Wanting to make sure tha
In the context of high compression rates applied to Joint Photographic Experts Group(JPEG)images through lossy compression techniques,image-blocking artifacts may manifest.This necessitates the restoration of the image to its original quality.The challenge lies in regenerating significantly compressed images into a state in which these become identifiable.Therefore,this study focuses on the restoration of JPEG images subjected to substantial degradation caused by maximum lossy compression using Generative Adversarial Networks(GAN).The generator in this network is based on theU-Net architecture.It features a newhourglass structure that preserves the characteristics of the deep layers.In addition,the network incorporates two loss functions to generate natural and high-quality images:Low Frequency(LF)loss and High Frequency(HF)loss.HF loss uses a pretrained VGG-16 network and is configured using a specific layer that best represents features.This can enhance the performance in the high-frequency region.In contrast,LF loss is used to handle the low-frequency region.The two loss functions facilitate the generation of images by the generator,which can mislead the discriminator while accurately generating high-and low-frequency regions.Consequently,by removing the blocking effects frommaximum lossy compressed images,images inwhich identities could be recognized are generated.This study represents a significant improvement over previous research in terms of the image resolution performance.
