DEFACTO: Image and Face Manipulation Dataset

@article{Mahfoudi2019DEFACTOIA,
  title={DEFACTO: Image and Face Manipulation Dataset},
  author={Ga{\"e}l Mahfoudi and Badr Tajini and F. Retraint and F. Morain-Nicolier and J. Dugelay and M. Pic},
  journal={2019 27th European Signal Processing Conference (EUSIPCO)},
  year={2019},
  pages={1-5}
}
This paper presents a novel dataset for image and face manipulation detection and localization called DEFACTO. The dataset was automatically generated using Microsoft common object in context database (MSCOCO) to produce semantically meaningful forgeries. Four categories of forgeries have been generated. Splicing forgeries which consist of inserting an external element into an image, copy-move forgeries where an element within an image is duplicated, object removal forgeries where objects are… Expand

Figures and Topics from this paper

Image Manipulation Detection by Multi-View Multi-Scale Supervision
TLDR
This paper addresses both aspects of image manipulation detection by multi-view feature learning and multi-scale supervision by exploiting noise distribution and boundary artifact surrounding tampered regions to learn semantic-agnostic and thus more generalizable features. Expand
Self-Adversarial Training incorporating Forgery Attention for Image Forgery Localization
TLDR
A self-adversarial training strategy and a reliable coarse-to-fine network that utilizes a self-attention mechanism to localize forged regions in forgery images to address the issue of insufficient training data. Expand
Robustness of Facial Recognition to GAN-based Face-morphing Attacks
TLDR
This work identifies two new, GAN-based methods that an attacker may already have in his arsenal that are evaluated against state-of-the-art facial recognition (FR) algorithms and demonstrates that improvements to the fidelity of FR algorithms do lead to a reduction in the success rate of attacks provided morphed images are considered when setting operational acceptance thresholds. Expand
Media Forensics and DeepFakes: An Overview
  • L. Verdoliva
  • Computer Science
  • IEEE Journal of Selected Topics in Signal Processing
  • 2020
TLDR
This review paper aims to present an analysis of the methods for visual media integrity verification, that is, the detection of manipulated images and videos, with special emphasis on the emerging phenomenon of deepfakes, fake media created through deep learning tools, and on modern data-driven forensic methods to fight them. Expand
Operation-wise Attention Network for Tampering Localization Fusion
TLDR
Evaluation in three publicly available forensics datasets demonstrates that the performance of the proposed approach is competitive, outperforming the individual forensics techniques as well as another recently proposed fusion framework in the majority of cases. Expand
A JPEG Forensic Detector for Color Bitmap Images
Identification of decompressed JPEG images, especially those compressed with high JPEG quality factors, is a challenging issue in image forensics. Furthermore, the applicability of the existing JPEGExpand
Does Melania Trump have a body double from the perspective of automatic face recognition?
In this paper, we explore whether automatic face recognition can help in verifying widespread misinformation on social media, particularly conspiracy theories that are based on the existence of bodyExpand
Potential advantages and limitations of using information fusion in media forensics—a discussion on the example of detecting face morphing attacks
TLDR
It is illustrated why the naive assumption that fusion would make the detection more reliable can fail in practice, and the constraints and limitations of the application of fusion are discussed and its impact to (media) forensics is reflected upon. Expand
Remote KYC: Attacks and Counter-Measures
TLDR
This paper analyzes those new kinds of face spoofing attacks and proposes a method to secure identity documents against both the traditional attacks and the new ones. Expand
Non-Semantic Evaluation of Image Forensics Tools: Methodology and Database
With the aim of evaluating image forensics tools, we propose a methodology to create forgeries traces, leaving intact the semantics of the image. Thus, the only forgery cues left are the specificExpand

References

SHOWING 1-10 OF 29 REFERENCES
CoMoFoD — New database for copy-move forgery detection
TLDR
A new database for a copy-move forgery detection (CMFD) that consist of 260 forged image sets, which includes forged image, two masks and original image is developed. Expand
An Evaluation of Popular Copy-Move Forgery Detection Approaches
TLDR
This paper created a challenging real-world copy-move dataset, and a software framework for systematic image manipulation, and examined the 15 most prominent feature sets, finding the keypoint-based features Sift and Surf as well as the block-based DCT, DWT, KPCA, PCA, and Zernike features perform very well. Expand
The PS-Battles Dataset - an Image Collection for Image Manipulation Detection
TLDR
The PS-Battles dataset is presented, 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. Expand
COVERAGE — A novel database for copy-move forgery detection
TLDR
Experimental results show that popular forgery detection methods perform poorly over COVERAGE, and the proposed sparsity based metric best correlates with human detection performance, and are released to the research community. Expand
Image Forgery Localization via Fine-Grained Analysis of CFA Artifacts
TLDR
A forensic tool able to discriminate between original and forged regions in an image captured by a digital camera is presented, based on a new feature measuring the presence of demosaicking artifacts at a local level and 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. Expand
A Data Set of Authentic and Spliced Image Blocks
TLDR
This report describes with details a data set of 1845 image blocks with a fixed size of 128 pixels x 128 pixels, extracted from images in the CalPhotos collection [CalPhotos'00], with a small number of additional images captured by digital cameras. Expand
Fighting Fake News: Image Splice Detection via Learned Self-Consistency
TLDR
A learning algorithm for detecting visual image manipulations that is trained only using a large dataset of real photographs to determine whether an image is self-consistent — that is, whether its content could have been produced by a single imaging pipeline. Expand
Learning Rich Features for Image Manipulation Detection
TLDR
A two-stream Faster R-CNN network is proposed and trained end-to-end to detect the tampered regions given a manipulated image and fuse features from the two streams through a bilinear pooling layer to further incorporate spatial co-occurrence of these two modalities. Expand
Detecting Image Splicing using Geometry Invariants and Camera Characteristics Consistency
TLDR
This is the first work detecting image splicing by verifying camera characteristic consistency from a single-channel image and shows a very promising accuracy, 87%, over a large data set of 363 natural and spliced images. Expand
A Deep Learning Approach to Universal Image Manipulation Detection Using a New Convolutional Layer
TLDR
A universal forensic approach to performing manipulation detection using deep learning that can automatically learn how to detect multiple image manipulations without relying on pre-selected features or any preprocessing is proposed. Expand
...
1
2
3
...