130 research outputs found
Image Forgery Localization via Fine-Grained Analysis of CFA Artifacts
In this paper, a forensic tool able to discriminate between original and forged regions in an image captured by a digital camera is presented. We make the assumption that the image is acquired using a Color Filter Array, and that tampering removes the artifacts due to the demosaicking algorithm. The proposed method is based on a new feature measuring the presence of demosaicking artifacts at a local level, and on a new statistical model allowing to derive the tampering probability of each 2 × 2 image block without requiring to know a priori the position of the forged region. Experimental results on different cameras equipped with different demosaicking algorithms demonstrate both the validity of the theoretical model and the effectiveness of our schem
An Overview on Image Forensics
The aim of this survey is to provide a comprehensive overview of the state of the art in the area of image forensics. These techniques have been designed to identify the source of a digital image or to determine whether the content is authentic or modified, without the knowledge of any prior information about the image under analysis (and thus are defined as passive). All these tools work by detecting the presence, the absence, or the incongruence of some traces intrinsically tied to the digital image by the acquisition device and by any other operation after its creation. The paper has been organized by classifying the tools according to the position in the history of the digital image in which the relative footprint is left: acquisition-based methods, coding-based methods, and editing-based schemes
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 Forensics for Forgery Detection using Contrast Enhancement and 3D Lighting
Nowadays the digital image plays an important role in human life. Due to large growth in the image processing techniques, with the availability of image modification tools any modification in the images can be done. These modifications cannot be recognized by human eyes. So Identification of the image integrity is very important in today’s life. Contrast and brightness of digital images can be adjusted by contrast enhancement. Move and paste type of images are Created by malicious person, in which contrast of one source image is enhanced to match the other source image. Here in this topic contrast enhancement technique is used which aimed at detecting image tampering has grown in different applications area such as law enforcement, surveillance. Also with the contrast enhancement, we propose an improved 3D lighting environment estimation method based on a more general surface reflection model. 3D lighting environment is an important clue in an image that can be used for image forgery detection. We intend to employ fully automatic face morphing and alignment algorithms. Also we intend to use face detection method to detect the face existence and 3D lighting environment estimation to check originality of human faces in the image
OCR Graph Features for Manipulation Detection in Documents
Detecting manipulations in digital documents is becoming increasingly
important for information verification purposes. Due to the proliferation of
image editing software, altering key information in documents has become widely
accessible. Nearly all approaches in this domain rely on a procedural approach,
using carefully generated features and a hand-tuned scoring system, rather than
a data-driven and generalizable approach. We frame this issue as a graph
comparison problem using the character bounding boxes, and propose a model that
leverages graph features using OCR (Optical Character Recognition). Our model
relies on a data-driven approach to detect alterations by training a random
forest classifier on the graph-based OCR features. We evaluate our algorithm's
forgery detection performance on dataset constructed from real business
documents with slight forgery imperfections. Our proposed model dramatically
outperforms the most closely-related document manipulation detection model on
this task
Exposing Fake Images with Forensic Similarity Graphs
We propose new image forgery detection and localization algorithms by
recasting these problems as graph-based community detection problems. To do
this, we introduce a novel abstract, graph-based representation of an image,
which we call the Forensic Similarity Graph, that captures key forensic
relationships among regions in the image. In this representation, small image
patches are represented by graph vertices with edges assigned according to the
forensic similarity between patches. Localized tampering introduces unique
structure into this graph, which aligns with a concept called ``community
structure'' in graph-theory literature. In the Forensic Similarity Graph,
communities correspond to the tampered and unaltered regions in the image. As a
result, forgery detection is performed by identifying whether multiple
communities exist, and forgery localization is performed by partitioning these
communities. We present two community detection techniques, adapted from
literature, to detect and localize image forgeries. We experimentally show that
our proposed community detection methods outperform existing state-of-the-art
forgery detection and localization methods, which do not capture such community
structure.Comment: 16 pages, under review at IEEE Journal of Selected Topics in Signal
Processin
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