3,679 research outputs found
Reliable and Fast Forgery Detection using FINE GRAINED approach
Forensic science encompassing the recovery and investigation of material found in digital devices, often in relation to computer crime. A digital forensic investigation commonly consists of 3 stages: acquisition or imaging of exhibits, analysis, and reporting. Previously, it is able to detect tampered images at high accuracy based on some carefully designed mechanisms,localization of the tampered regions in a fake image still presents many challenges, especially when the type of tampering operation is unknown. Later on, necessary to integrate different forensic approaches in order to obtain better localization performance. However, several important issues have not been comprehensively studied, to improve/readjust proper forensic approaches, and to fuse the detection results of different forensic approaches to obtain good localization results. In this paper, we propose a framework to improve the performance of forgery localization via implementing tampering possibility maps along with fusion based technique. In the proposed framework, we first select and improve existing forensic approaches, i.e., copy-move forgery detector and statistical feature based approach, and then improve their results to obtain tampering possibility maps
A Full-Image Full-Resolution End-to-End-Trainable CNN Framework for Image Forgery Detection
Due to limited computational and memory resources, current deep learning
models accept only rather small images in input, calling for preliminary image
resizing. This is not a problem for high-level vision problems, where
discriminative features are barely affected by resizing. On the contrary, in
image forensics, resizing tends to destroy precious high-frequency details,
impacting heavily on performance. One can avoid resizing by means of patch-wise
processing, at the cost of renouncing whole-image analysis. In this work, we
propose a CNN-based image forgery detection framework which makes decisions
based on full-resolution information gathered from the whole image. Thanks to
gradient checkpointing, the framework is trainable end-to-end with limited
memory resources and weak (image-level) supervision, allowing for the joint
optimization of all parameters. Experiments on widespread image forensics
datasets prove the good performance of the proposed approach, which largely
outperforms all baselines and all reference methods.Comment: 13 pages, 12 figures, journa
D-Unet: A Dual-encoder U-Net for Image Splicing Forgery Detection and Localization
Recently, many detection methods based on convolutional neural networks
(CNNs) have been proposed for image splicing forgery detection. Most of these
detection methods focus on the local patches or local objects. In fact, image
splicing forgery detection is a global binary classification task that
distinguishes the tampered and non-tampered regions by image fingerprints.
However, some specific image contents are hardly retained by CNN-based
detection networks, but if included, would improve the detection accuracy of
the networks. To resolve these issues, we propose a novel network called
dual-encoder U-Net (D-Unet) for image splicing forgery detection, which employs
an unfixed encoder and a fixed encoder. The unfixed encoder autonomously learns
the image fingerprints that differentiate between the tampered and non-tampered
regions, whereas the fixed encoder intentionally provides the direction
information that assists the learning and detection of the network. This
dual-encoder is followed by a spatial pyramid global-feature extraction module
that expands the global insight of D-Unet for classifying the tampered and
non-tampered regions more accurately. In an experimental comparison study of
D-Unet and state-of-the-art methods, D-Unet outperformed the other methods in
image-level and pixel-level detection, without requiring pre-training or
training on a large number of forgery images. Moreover, it was stably robust to
different attacks.Comment: 13 pages, 13 figure
Image and Video Forensics
Nowadays, images and videos have become the main modalities of information being exchanged in everyday life, and their pervasiveness has led the image forensics community to question their reliability, integrity, confidentiality, and security. Multimedia contents are generated in many different ways through the use of consumer electronics and high-quality digital imaging devices, such as smartphones, digital cameras, tablets, and wearable and IoT devices. The ever-increasing convenience of image acquisition has facilitated instant distribution and sharing of digital images on digital social platforms, determining a great amount of exchange data. Moreover, the pervasiveness of powerful image editing tools has allowed the manipulation of digital images for malicious or criminal ends, up to the creation of synthesized images and videos with the use of deep learning techniques. In response to these threats, the multimedia forensics community has produced major research efforts regarding the identification of the source and the detection of manipulation. In all cases (e.g., forensic investigations, fake news debunking, information warfare, and cyberattacks) where images and videos serve as critical evidence, forensic technologies that help to determine the origin, authenticity, and integrity of multimedia content can become essential tools. This book aims to collect a diverse and complementary set of articles that demonstrate new developments and applications in image and video forensics to tackle new and serious challenges to ensure media authenticity
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