In this work, we deal with the problem of re compression based image forgery
detection, where some regions of an image are modified illegitimately, hence
giving rise to presence of dual compression characteristics within a single
image. There have been some significant researches in this direction, in the
last decade. However, almost all existing techniques fail to detect this form
of forgery, when the first compression factor is greater than the second. We
address this problem in re compression based forgery detection, here Recently,
Machine Learning techniques have started gaining a lot of importance in the
domain of digital image forensics. In this work, we propose a Convolution
Neural Network based deep learning architecture, which is capable of detecting
the presence of re compression based forgery in JPEG images. The proposed
architecture works equally efficiently, even in cases where the first
compression ratio is greater than the second. In this work, we also aim to
localize the regions of image manipulation based on re compression features,
using the trained neural network. Our experimental results prove that the
proposed method outperforms the state of the art, with respect to forgery
detection and localization accuracy.

本研究通过使用基于重压缩的伪造检测方法，利用卷积神经网络架构检测 JPEG 图像中的重压缩伪造，并成功定位了图像操纵的区域。实验证明，该方法在伪造检测和定位的准确性方面优于现有技术。

基于重新压缩的 JPEG 篡改检测与定位：利用深度神经网络消除压缩因子依赖

Recompression Based JPEG Tamper Detection and Localization Using Deep  Neural Network Eliminating Compression Factor Dependency

We performed pairwise comparisons by human raters of JPEG images from
MozJPEG, libjpeg-turbo and our new Jpegli encoder. When compressing images at a
quality similar to libjpeg-turbo quality 95, the Jpegli images were 54% likely
to be preferred over both libjpeg-turbo and MozJPEG images, but used only 2.8
bits per pixel compared to libjpeg-turbo and MozJPEG that used 3.8 and 3.5 bits
per pixel respectively. The raw ratings and source images are publicly
available for further analysis and study.

我们通过人工评比比较了由 MozJPEG、libjpeg-turbo 和我们的新 Jpegli 编码器所编码的 JPEG 图像。当图像质量接近 libjpeg-turbo 的 95 质量时，Jpegli 图像优于 libjpeg-turbo 和 MozJPEG 图像的可能性达到 54%，但仅使用了每像素 2.8 比特，而 libjpeg-turbo 和 MozJPEG 分别使用了 3.8 比特和 3.5 比特。原始评分和源图像可供进一步分析和研究。

用户更喜欢 Jpegli 而非同样大小的 libjpeg-turbo 或 MozJPEG

Users prefer Jpegli over same-sized libjpeg-turbo or MozJPEG

We propose a new scheme to re-compress JPEG images in a lossless way. Using a
JPEG image as an input the algorithm partially decodes the signal to obtain
quantized DCT coefficients and then re-compress them in a more effective way.

我们提出了一种新的方案，以无损的方式重新压缩 JPEG 图像。使用 JPEG 图像作为输入，该算法部分解码信号以获得量化的 DCT 系数，然后以更有效的方式重新压缩它们。

基于实验的 JPEG 图像再压缩的分布预测

Distribution prediction for image compression: An experimental  re-compressor for JPEG images

Unlike hiding bit-level messages, hiding image-level messages is more
challenging, which requires large capacity, high imperceptibility, and high
security. Although recent advances in hiding image-level messages have been
remarkable, existing schemes are limited to lossless spatial images as covers
and cannot be directly applied to JPEG images, the ubiquitous lossy format
images in daily life. The difficulties of migration are caused by the lack of
targeted design and the loss of details due to lossy decompression and
re-compression. Considering that taking DCT densely on $8\times8$ image patches
is the core of the JPEG compression standard, we design a novel model called
\textsf{EFDR}, which can comprehensively \underline{E}xploit
\underline{F}ine-grained \underline{D}CT \underline{R}epresentations and embed
the secret image into quantized DCT coefficients to avoid the lossy process.
Specifically, we transform the JPEG cover image and hidden secret image into
fine-grained DCT representations that compact the frequency and are associated
with the inter-block and intra-block correlations. Subsequently, the
fine-grained DCT representations are further enhanced by a sub-band features
enhancement module. Afterward, a transformer-based invertibility module is
designed to fuse enhanced sub-band features. Such a design enables a
fine-grained self-attention on each sub-band and captures long-range
dependencies while maintaining excellent reversibility for hiding and recovery.
To our best knowledge, this is the first attempt to embed a color image of
equal size in a color JPEG image. Extensive experiments demonstrate the
effectiveness of our \textsf{EFDR} with superior performance.

该篇论文介绍了一种名为 EFDR 的基于 DCT 变换的图像藏密算法，通过对 JPEG 格式图像的子带进行特征增强，实现将彩色图像嵌入到 JPEG 图像中的目的。实验结果表明，该算法在载密量、抗失真等方面表现出优异的性能。