CNN-based first quantization estimation of double compressed JPEG images

Multiple JPEG compressions leave artifacts in digital images: residual traces that could be exploited in forensics investigations to recover information about the device employed for acquisition or image editing software. In this paper, a novel First Quantization Estimation (FQE) algorithm based on convolutional neural networks (CNNs) is proposed. In particular, a solution based on an ensemble of CNNs was developed in conjunction with specific regularization strategies exploiting assumptions about neighboring element values of the quantization matrix to be inferred. Mostly designed to work in the aligned case, the solution was tested in challenging scenarios involving different input patch sizes, quantization matrices (both standard and custom) and datasets (i.e., RAISE and UCID collections). Comparisons with state-of-the-art solutions confirmed the effectiveness of the presented solution demonstrating for the first time to cover the widest combinations of parameters of double JPEG compressions

First Quantization Estimation by a Robust Data Exploitation Strategy of DCT Coefficients

It is well known that the JPEG compression pipeline leaves residual traces in the compressed images that are useful for forensic investigations. Through the analysis of such insights the history of a digital image can be reconstructed by means of First Quantization Estimations (FQE), often employed for the camera model identification (CMI) task. In this paper, a novel FQE technique for JPEG double compressed images is proposed which employs a mixed approach based on Machine Learning and statistical analysis. The proposed method was designed to work in the aligned case (i.e., $8 imes 8$ JPEG grid is not misaligned among the various compressions) and demonstrated to be able to work effectively in different challenging scenarios (small input patches, custom quantization tables) without strong a-priori assumptions, surpassing state-of-the-art solutions. Finally, an in-depth analysis on the impact of image input sizes, dataset image resolutions, custom quantization tables and different Discrete Cosine Transform (DCT) implementations was carried out

Estimating Previous Quantization Factors on Multiple JPEG Compressed Images

The JPEG compression algorithm has proven to be efficient in saving storage and preserving image quality thus becoming extremely popular. On the other hand, the overall process leaves traces into encoded signals which are typically exploited for forensic purposes: for instance, the compression parameters of the acquisition device (or editing software) could be inferred. To this aim, in this paper a novel technique to estimate “previous” JPEG quantization factors on images compressed multiple times, in the aligned case by analyzing statistical traces hidden on Discrete Cosine Transform (DCT) histograms is exploited. Experimental results on double, triple and quadruple compressed images, demonstrate the effectiveness of the proposed technique while unveiling further interesting insights

Quality Classified Image Analysis with Application to Face Detection and Recognition

Motion blur, out of focus, insufficient spatial resolution, lossy compression and many other factors can all cause an image to have poor quality. However, image quality is a largely ignored issue in traditional pattern recognition literature. In this paper, we use face detection and recognition as case studies to show that image quality is an essential factor which will affect the performances of traditional algorithms. We demonstrated that it is not the image quality itself that is the most important, but rather the quality of the images in the training set should have similar quality as those in the testing set. To handle real-world application scenarios where images with different kinds and severities of degradation can be presented to the system, we have developed a quality classified image analysis framework to deal with images of mixed qualities adaptively. We use deep neural networks first to classify images based on their quality classes and then design a separate face detector and recognizer for images in each quality class. We will present experimental results to show that our quality classified framework can accurately classify images based on the type and severity of image degradations and can significantly boost the performances of state-of-the-art face detector and recognizer in dealing with image datasets containing mixed quality images.Comment: 6 page