29 research outputs found
A JPEG Corner Artifact from Directed Rounding of DCT Coefficients
JPEG compression introduces a number of well known artifacts including blocking and ringing. We describe a lesser known or understood artifact consisting of a slightly darker or lighter pixel in the corner of 8 x 8 pixel blocks. This artifact is introduced by the directed rounding of DCT coefficients. In particular, we show that DCT coefficients that are uniformly rounded down or up (but not to the nearest neighbor) give rise to this artifact. An analysis of thousands of different camera models reveals that this artifact is present in approximately 61% of cameras. We also propose a simple filtering technique for removing this artifact
To Beta or Not To Beta: Information Bottleneck for DigitaL Image Forensics
We consider an information theoretic approach to address the problem of
identifying fake digital images. We propose an innovative method to formulate
the issue of localizing manipulated regions in an image as a deep
representation learning problem using the Information Bottleneck (IB), which
has recently gained popularity as a framework for interpreting deep neural
networks. Tampered images pose a serious predicament since digitized media is a
ubiquitous part of our lives. These are facilitated by the easy availability of
image editing software and aggravated by recent advances in deep generative
models such as GANs. We propose InfoPrint, a computationally efficient solution
to the IB formulation using approximate variational inference and compare it to
a numerical solution that is computationally expensive. Testing on a number of
standard datasets, we demonstrate that InfoPrint outperforms the
state-of-the-art and the numerical solution. Additionally, it also has the
ability to detect alterations made by inpainting GANs.Comment: 10 page
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
Training CNNs in Presence of JPEG Compression: Multimedia Forensics vs Computer Vision
Convolutional Neural Networks (CNNs) have proved very accurate in multiple
computer vision image classification tasks that required visual inspection in
the past (e.g., object recognition, face detection, etc.). Motivated by these
astonishing results, researchers have also started using CNNs to cope with
image forensic problems (e.g., camera model identification, tampering
detection, etc.). However, in computer vision, image classification methods
typically rely on visual cues easily detectable by human eyes. Conversely,
forensic solutions rely on almost invisible traces that are often very subtle
and lie in the fine details of the image under analysis. For this reason,
training a CNN to solve a forensic task requires some special care, as common
processing operations (e.g., resampling, compression, etc.) can strongly hinder
forensic traces. In this work, we focus on the effect that JPEG has on CNN
training considering different computer vision and forensic image
classification problems. Specifically, we consider the issues that rise from
JPEG compression and misalignment of the JPEG grid. We show that it is
necessary to consider these effects when generating a training dataset in order
to properly train a forensic detector not losing generalization capability,
whereas it is almost possible to ignore these effects for computer vision
tasks