954 research outputs found
Aligned and Non-Aligned Double JPEG Detection Using Convolutional Neural Networks
Due to the wide diffusion of JPEG coding standard, the image forensic
community has devoted significant attention to the development of double JPEG
(DJPEG) compression detectors through the years. The ability of detecting
whether an image has been compressed twice provides paramount information
toward image authenticity assessment. Given the trend recently gained by
convolutional neural networks (CNN) in many computer vision tasks, in this
paper we propose to use CNNs for aligned and non-aligned double JPEG
compression detection. In particular, we explore the capability of CNNs to
capture DJPEG artifacts directly from images. Results show that the proposed
CNN-based detectors achieve good performance even with small size images (i.e.,
64x64), outperforming state-of-the-art solutions, especially in the non-aligned
case. Besides, good results are also achieved in the commonly-recognized
challenging case in which the first quality factor is larger than the second
one.Comment: Submitted to Journal of Visual Communication and Image Representation
(first submission: March 20, 2017; second submission: August 2, 2017
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., 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
- …