142 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
CoMoFoD #x2014; New database for copy-move forgery detection
Due to the availability of many sophisticated image processing tools, a digital image forgery is nowadays very often used. One of the common forgery method is a copy-move forgery, where part of an image is copied to another location in the same image with the aim of hiding or adding some image content. Numerous algorithms have been proposed for a copy-move forgery detection (CMFD), but there exist only few benchmarking databases for algorithms evaluation. We developed new database for a CMFD that consist of 260 forged image sets. Every image set includes forged image, two masks and original image. Images are grouped in 5 categories according to applied manipulation: translation, rotation, scaling, combination and distortion. Also, postprocessing methods, such as JPEG compression, blurring, noise adding, color reduction etc., are applied at all forged and original images. In this paper we present database organization and content, creation of forged images, postprocessing methods, and database testing. CoMoFoD database is available at http://www.vcl.fer.hr/comofodMinistry of Science, Education and Sport, China; project numbers: 036-0361630-1635 and 036-0361630-164
Learning Rich Features for Image Manipulation Detection
Image manipulation detection is different from traditional semantic object
detection because it pays more attention to tampering artifacts than to image
content, which suggests that richer features need to be learned. We propose a
two-stream Faster R-CNN network and train it endto- end to detect the tampered
regions given a manipulated image. One of the two streams is an RGB stream
whose purpose is to extract features from the RGB image input to find tampering
artifacts like strong contrast difference, unnatural tampered boundaries, and
so on. The other is a noise stream that leverages the noise features extracted
from a steganalysis rich model filter layer to discover the noise inconsistency
between authentic and tampered regions. We then fuse features from the two
streams through a bilinear pooling layer to further incorporate spatial
co-occurrence of these two modalities. Experiments on four standard image
manipulation datasets demonstrate that our two-stream framework outperforms
each individual stream, and also achieves state-of-the-art performance compared
to alternative methods with robustness to resizing and compression.Comment: CVPR 2018 Camera Read
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
Lightweight MobileNet Model for Image Tempering Detection
In recent years, there has been a wide range of image manipulation identification challenges and an overview of image tampering detection and the relevance of applying deep learning models such as CNN and MobileNet for this purpose. The discussion then delves into the construction and setup of these models, which includes a block diagram as well as mathematical calculations for each layer. A literature study on Image tampering detection is also included in the discussion, comparing and contrasting various articles and their methodologies. The study then moves on to training and assessment datasets, such as the CASIA v2 dataset, and performance indicators like as accuracy and loss. Lastly, the performance characteristics of the MobileNet and CNN designs are compared. This work focuses on Image tampering detection using convolutional neural networks (CNNs) and the MobileNet architecture. We reviewed the MobileNet architecture's setup and block diagram, as well as its application to Image tampering detection. We also looked at significant literature on Image manipulation detection, such as major studies and their methodologies. Using the CASIA v2 dataset, we evaluated the performance of MobileNet and CNN architectures in terms of accuracy and loss. This paper offered an overview of the usage of deep learning and CNN architectures for image tampering detection and proved their accuracy in detecting manipulated images
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