1,541 research outputs found
Image forgery detection using textural features and deep learning
La croissance exponentielle et les progrès de la technologie ont rendu très pratique le partage de données visuelles, d'images et de données vidéo par le biais d’une vaste prépondérance de platesformes disponibles. Avec le développement rapide des technologies Internet et multimédia, l’efficacité de la gestion et du stockage, la rapidité de transmission et de partage, l'analyse en temps réel et le traitement des ressources multimédias numériques sont progressivement devenus un élément indispensable du travail et de la vie de nombreuses personnes. Sans aucun doute, une telle croissance technologique a rendu le forgeage de données visuelles relativement facile et réaliste sans laisser de traces évidentes. L'abus de ces données falsifiées peut tromper le public et répandre la désinformation parmi les masses.
Compte tenu des faits mentionnés ci-dessus, la criminalistique des images doit être utilisée pour authentifier et maintenir l'intégrité des données visuelles. Pour cela, nous proposons une technique de détection passive de falsification d'images basée sur les incohérences de texture et de bruit introduites dans une image du fait de l'opération de falsification.
De plus, le réseau de détection de falsification d'images (IFD-Net) proposé utilise une architecture basée sur un réseau de neurones à convolution (CNN) pour classer les images comme falsifiées ou vierges. Les motifs résiduels de texture et de bruit sont extraits des images à l'aide du motif binaire local (LBP) et du modèle Noiseprint. Les images classées comme forgées sont ensuite utilisées pour mener des expériences afin d'analyser les difficultés de localisation des pièces forgées dans ces images à l'aide de différents modèles de segmentation d'apprentissage en profondeur.
Les résultats expérimentaux montrent que l'IFD-Net fonctionne comme les autres méthodes de détection de falsification d'images sur l'ensemble de données CASIA v2.0. Les résultats discutent également des raisons des difficultés de segmentation des régions forgées dans les images du jeu de données CASIA v2.0.The exponential growth and advancement of technology have made it quite convenient for people to share visual data, imagery, and video data through a vast preponderance of available platforms. With the rapid development of Internet and multimedia technologies, performing efficient storage and management, fast transmission and sharing, real-time analysis, and processing of digital media resources has gradually become an indispensable part of many people’s work and life. Undoubtedly such technological growth has made forging visual data relatively easy and realistic without leaving any obvious visual clues. Abuse of such tampered data can deceive the public and spread misinformation amongst the masses. Considering the facts mentioned above, image forensics must be used to authenticate and maintain the integrity of visual data. For this purpose, we propose a passive image forgery detection technique based on textural and noise inconsistencies introduced in an image because of the tampering operation.
Moreover, the proposed Image Forgery Detection Network (IFD-Net) uses a Convolution Neural Network (CNN) based architecture to classify the images as forged or pristine. The textural and noise residual patterns are extracted from the images using Local Binary Pattern (LBP) and the Noiseprint model. The images classified as forged are then utilized to conduct experiments to analyze the difficulties in localizing the forged parts in these images using different deep learning segmentation models.
Experimental results show that both the IFD-Net perform like other image forgery detection methods on the CASIA v2.0 dataset. The results also discuss the reasons behind the difficulties in segmenting the forged regions in the images of the CASIA v2.0 dataset
To Beta or Not To Beta: Information Bottleneck for DigitaL Image Forensics
We consider an information theoretic approach to address the problem of
identifying fake digital images. We propose an innovative method to formulate
the issue of localizing manipulated regions in an image as a deep
representation learning problem using the Information Bottleneck (IB), which
has recently gained popularity as a framework for interpreting deep neural
networks. Tampered images pose a serious predicament since digitized media is a
ubiquitous part of our lives. These are facilitated by the easy availability of
image editing software and aggravated by recent advances in deep generative
models such as GANs. We propose InfoPrint, a computationally efficient solution
to the IB formulation using approximate variational inference and compare it to
a numerical solution that is computationally expensive. Testing on a number of
standard datasets, we demonstrate that InfoPrint outperforms the
state-of-the-art and the numerical solution. Additionally, it also has the
ability to detect alterations made by inpainting GANs.Comment: 10 page
EXIF as Language: Learning Cross-Modal Associations Between Images and Camera Metadata
We learn a visual representation that captures information about the camera
that recorded a given photo. To do this, we train a multimodal embedding
between image patches and the EXIF metadata that cameras automatically insert
into image files. Our model represents this metadata by simply converting it to
text and then processing it with a transformer. The features that we learn
significantly outperform other self-supervised and supervised features on
downstream image forensics and calibration tasks. In particular, we
successfully localize spliced image regions "zero shot" by clustering the
visual embeddings for all of the patches within an image.Comment: Project link: http://hellomuffin.github.io/exif-as-languag
Progressive Feedback-Enhanced Transformer for Image Forgery Localization
Blind detection of the forged regions in digital images is an effective
authentication means to counter the malicious use of local image editing
techniques. Existing encoder-decoder forensic networks overlook the fact that
detecting complex and subtle tampered regions typically requires more feedback
information. In this paper, we propose a Progressive FeedbACk-enhanced
Transformer (ProFact) network to achieve coarse-to-fine image forgery
localization. Specifically, the coarse localization map generated by an initial
branch network is adaptively fed back to the early transformer encoder layers
for enhancing the representation of positive features while suppressing
interference factors. The cascaded transformer network, combined with a
contextual spatial pyramid module, is designed to refine discriminative
forensic features for improving the forgery localization accuracy and
reliability. Furthermore, we present an effective strategy to automatically
generate large-scale forged image samples close to real-world forensic
scenarios, especially in realistic and coherent processing. Leveraging on such
samples, a progressive and cost-effective two-stage training protocol is
applied to the ProFact network. The extensive experimental results on nine
public forensic datasets show that our proposed localizer greatly outperforms
the state-of-the-art on the generalization ability and robustness of image
forgery localization. Code will be publicly available at
https://github.com/multimediaFor/ProFact
MSMG-Net: Multi-scale Multi-grained Supervised Metworks for Multi-task Image Manipulation Detection and Localization
With the rapid advances of image editing techniques in recent years, image
manipulation detection has attracted considerable attention since the
increasing security risks posed by tampered images. To address these
challenges, a novel multi-scale multi-grained deep network (MSMG-Net) is
proposed to automatically identify manipulated regions. In our MSMG-Net, a
parallel multi-scale feature extraction structure is used to extract
multi-scale features. Then the multi-grained feature learning is utilized to
perceive object-level semantics relation of multi-scale features by introducing
the shunted self-attention. To fuse multi-scale multi-grained features, global
and local feature fusion block are designed for manipulated region segmentation
by a bottom-up approach and multi-level feature aggregation block is designed
for edge artifacts detection by a top-down approach. Thus, MSMG-Net can
effectively perceive the object-level semantics and encode the edge artifact.
Experimental results on five benchmark datasets justify the superior
performance of the proposed method, outperforming state-of-the-art manipulation
detection and localization methods. Extensive ablation experiments and feature
visualization demonstrate the multi-scale multi-grained learning can present
effective visual representations of manipulated regions. In addition, MSMG-Net
shows better robustness when various post-processing methods further manipulate
images
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