202,025 research outputs found
The PS-Battles Dataset - an Image Collection for Image Manipulation Detection
The boost of available digital media has led to a significant increase in
derivative work. With tools for manipulating objects becoming more and more
mature, it can be very difficult to determine whether one piece of media was
derived from another one or tampered with. As derivations can be done with
malicious intent, there is an urgent need for reliable and easily usable
tampering detection methods. However, even media considered semantically
untampered by humans might have already undergone compression steps or light
post-processing, making automated detection of tampering susceptible to false
positives. In this paper, we present the PS-Battles dataset which is gathered
from a large community of image manipulation enthusiasts and provides a basis
for media derivation and manipulation detection in the visual domain. The
dataset consists of 102'028 images grouped into 11'142 subsets, each containing
the original image as well as a varying number of manipulated derivatives.Comment: The dataset introduced in this paper can be found on
https://github.com/dbisUnibas/PS-Battle
Boosting Image Forgery Detection using Resampling Features and Copy-move analysis
Realistic image forgeries involve a combination of splicing, resampling,
cloning, region removal and other methods. While resampling detection
algorithms are effective in detecting splicing and resampling, copy-move
detection algorithms excel in detecting cloning and region removal. In this
paper, we combine these complementary approaches in a way that boosts the
overall accuracy of image manipulation detection. We use the copy-move
detection method as a pre-filtering step and pass those images that are
classified as untampered to a deep learning based resampling detection
framework. Experimental results on various datasets including the 2017 NIST
Nimble Challenge Evaluation dataset comprising nearly 10,000 pristine and
tampered images shows that there is a consistent increase of 8%-10% in
detection rates, when copy-move algorithm is combined with different resampling
detection algorithms
SRU-NET: SOBEL RESIDUAL U-NET FOR IMAGE MANIPULATION DETECTION
Recently, most successful image manipulation detection methods have been based on convolutional neural networks (CNNs). Nevertheless, Existing CNN methods have limited abilities. CNN-based detection networks tend to extract signal features strongly related to content. However, image manipulation detection tends to extract weak signal features that are weakly related to content. To address this issue, We propose a novel Sobel residual neural network with adaptive central difference convolution, an extension of the classical U-Net architecture, for image manipulation detection. Adaptive central differential convolution can capture the essential attributes of an image by gathering intensity and gradient information. Sobel residual gradient block can capture forgery edge discriminative details. Extensive experimental results show that our method can significantly improve the accuracy of localising the forged region compared with the state-of-the-art methods
Digital Restoration of Ancient Papyri
Image processing can be used for digital restoration of ancient papyri, that
is, for a restoration performed on their digital images. The digital
manipulation allows reducing the background signals and enhancing the
readability of texts. In the case of very old and damaged documents, this is
fundamental for identification of the patterns of letters. Some examples of
restoration, obtained with an image processing which uses edges detection and
Fourier filtering, are shown. One of them concerns 7Q5 fragment of the Dead Sea
Scrolls
Image Splicing Detection Based on Demosaicking and Wavelet Transformation
Image splicing is a form of digital image manipulation by combining two or more image into a new image. The application was developed through a passive approach using demosaicking and wavelet transformation method. This research purposed a method to implement the demosaicking and wavelet transform for digital image forgery detection with a passive approach. This research shows that (1) demosaicking can be used as a comparison image in forgery detection; (2) the application of demosaicking and wavelet transformation can improve the quality of the input image (3) demosaicking and wavelet algorithm are able to estimate whether the input image is real or fake image with a passive approach and estimate the manipulation area from the input image
On the Effectiveness of Image Manipulation Detection in the Age of Social Media
Image manipulation detection algorithms designed to identify local anomalies
often rely on the manipulated regions being ``sufficiently'' different from the
rest of the non-tampered regions in the image. However, such anomalies might
not be easily identifiable in high-quality manipulations, and their use is
often based on the assumption that certain image phenomena are associated with
the use of specific editing tools. This makes the task of manipulation
detection hard in and of itself, with state-of-the-art detectors only being
able to detect a limited number of manipulation types. More importantly, in
cases where the anomaly assumption does not hold, the detection of false
positives in otherwise non-manipulated images becomes a serious problem.
To understand the current state of manipulation detection, we present an
in-depth analysis of deep learning-based and learning-free methods, assessing
their performance on different benchmark datasets containing tampered and
non-tampered samples. We provide a comprehensive study of their suitability for
detecting different manipulations as well as their robustness when presented
with non-tampered data. Furthermore, we propose a novel deep learning-based
pre-processing technique that accentuates the anomalies present in manipulated
regions to make them more identifiable by a variety of manipulation detection
methods. To this end, we introduce an anomaly enhancement loss that, when used
with a residual architecture, improves the performance of different detection
algorithms with a minimal introduction of false positives on the
non-manipulated data.
Lastly, we introduce an open-source manipulation detection toolkit comprising
a number of standard detection algorithms
Auto-Focus Contrastive Learning for Image Manipulation Detection
Generally, current image manipulation detection models are simply built on
manipulation traces. However, we argue that those models achieve sub-optimal
detection performance as it tends to: 1) distinguish the manipulation traces
from a lot of noisy information within the entire image, and 2) ignore the
trace relations among the pixels of each manipulated region and its
surroundings. To overcome these limitations, we propose an Auto-Focus
Contrastive Learning (AF-CL) network for image manipulation detection. It
contains two main ideas, i.e., multi-scale view generation (MSVG) and trace
relation modeling (TRM). Specifically, MSVG aims to generate a pair of views,
each of which contains the manipulated region and its surroundings at a
different scale, while TRM plays a role in modeling the trace relations among
the pixels of each manipulated region and its surroundings for learning the
discriminative representation. After learning the AF-CL network by minimizing
the distance between the representations of corresponding views, the learned
network is able to automatically focus on the manipulated region and its
surroundings and sufficiently explore their trace relations for accurate
manipulation detection. Extensive experiments demonstrate that, compared to the
state-of-the-arts, AF-CL provides significant performance improvements, i.e.,
up to 2.5%, 7.5%, and 0.8% F1 score, on CAISA, NIST, and Coverage datasets,
respectively
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