136 research outputs found
Find it! Fraud Detection Contest Report
International audienceThis paper describes the ICPR2018 fraud detection contest, its data set, evaluation methodology, as well as the different methods submitted by the participants to tackle the predefined tasks. Forensics research is quite a sensitive topic. Data are either private or unlabeled and most of related works are evaluated on private datasets with a restricted access. This restriction has two major consequences: results cannot be reproduced and no benchmarking can be done between every approach. This contest was conceived in order to address these drawbacks. Two tasks were proposed: detecting documents containing at least one forgery in a flow of documents and spotting and localizing these forgeries within documents. An original dataset composed of images and texts of French receipts was provided to participants. The results they obtained are presented and discussed
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
Deep neural networks based error level analysis for lossless image compression based forgery detection.
The proposed model is implemented in deep learning based on counterfeit feature extraction and Error Level Analysis (ELA) techniques. Error level analysis is used to improve the efficiency of distinguishing copy-move images produced by Deep Fake from the real ones. Error Level Analysis is used on images in-depth for identifying whether the photograph has long passed through changing. This Model uses CNN on the dataset of images for training and to test the dataset for identifying the forged image. Convolution neural network (CNN) can extract the counterfeit attribute and detect if images are false. In the proposed approach after the tests were carried out, it is displayed with the pie chart representation based on percentage the image is detected. It also detects different image compression ratios using the ELA process. The results of the assessments display the effectiveness of the proposed method
Drawing, Handwriting Processing Analysis: New Advances and Challenges
International audienceDrawing and handwriting are communicational skills that are fundamental in geopolitical, ideological and technological evolutions of all time. drawingand handwriting are still useful in defining innovative applications in numerous fields. In this regard, researchers have to solve new problems like those related to the manner in which drawing and handwriting become an efficient way to command various connected objects; or to validate graphomotor skills as evident and objective sources of data useful in the study of human beings, their capabilities and their limits from birth to decline
Sharp Multiple Instance Learning for DeepFake Video Detection
With the rapid development of facial manipulation techniques, face forgery
has received considerable attention in multimedia and computer vision community
due to security concerns. Existing methods are mostly designed for single-frame
detection trained with precise image-level labels or for video-level prediction
by only modeling the inter-frame inconsistency, leaving potential high risks
for DeepFake attackers. In this paper, we introduce a new problem of partial
face attack in DeepFake video, where only video-level labels are provided but
not all the faces in the fake videos are manipulated. We address this problem
by multiple instance learning framework, treating faces and input video as
instances and bag respectively. A sharp MIL (S-MIL) is proposed which builds
direct mapping from instance embeddings to bag prediction, rather than from
instance embeddings to instance prediction and then to bag prediction in
traditional MIL. Theoretical analysis proves that the gradient vanishing in
traditional MIL is relieved in S-MIL. To generate instances that can accurately
incorporate the partially manipulated faces, spatial-temporal encoded instance
is designed to fully model the intra-frame and inter-frame inconsistency, which
further helps to promote the detection performance. We also construct a new
dataset FFPMS for partially attacked DeepFake video detection, which can
benefit the evaluation of different methods at both frame and video levels.
Experiments on FFPMS and the widely used DFDC dataset verify that S-MIL is
superior to other counterparts for partially attacked DeepFake video detection.
In addition, S-MIL can also be adapted to traditional DeepFake image detection
tasks and achieve state-of-the-art performance on single-frame datasets.Comment: Accepted at ACM MM 2020. 11 pages, 8 figures, with appendi
Investigating the effectiveness of novel support vector neural network for anomaly detection in digital forensics data
As criminal activity increasingly relies on digital devices, the field of digital forensics plays a vital role in identifying and investigating criminals. In this paper, we addressed the problem of anomaly detection in digital forensics data. Our objective was to propose an effective approach for identifying suspicious patterns and activities that could indicate criminal behavior. To achieve this, we introduce a novel method called the Novel Support Vector Neural Network (NSVNN). We evaluated the performance of the NSVNN by conducting experiments on a real-world dataset of digital forensics data. The dataset consisted of various features related to network activity, system logs, and file metadata. Through our experiments, we compared the NSVNN with several existing anomaly detection algorithms, including Support Vector Machines (SVM) and neural networks. We measured and analyzed the performance of each algorithm in terms of the accuracy, precision, recall, and F1-score. Furthermore, we provide insights into the specific features that contribute significantly to the detection of anomalies. Our results demonstrated that the NSVNN method outperformed the existing algorithms in terms of anomaly detection accuracy. We also highlight the interpretability of the NSVNN model by analyzing the feature importance and providing insights into the decision-making process. Overall, our research contributes to the field of digital forensics by proposing a novel approach, the NSVNN, for anomaly detection. We emphasize the importance of both performance evaluation and model interpretability in this context, providing practical insights for identifying criminal behavior in digital forensics investigations. © 2023 by the authors
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