5 research outputs found
A Visually Attentive Splice Localization Network with Multi-Domain Feature Extractor and Multi-Receptive Field Upsampler
Image splice manipulation presents a severe challenge in today's society.
With easy access to image manipulation tools, it is easier than ever to modify
images that can mislead individuals, organizations or society. In this work, a
novel, "Visually Attentive Splice Localization Network with Multi-Domain
Feature Extractor and Multi-Receptive Field Upsampler" has been proposed. It
contains a unique "visually attentive multi-domain feature extractor" (VA-MDFE)
that extracts attentional features from the RGB, edge and depth domains. Next,
a "visually attentive downsampler" (VA-DS) is responsible for fusing and
downsampling the multi-domain features. Finally, a novel "visually attentive
multi-receptive field upsampler" (VA-MRFU) module employs multiple receptive
field-based convolutions to upsample attentional features by focussing on
different information scales. Experimental results conducted on the public
benchmark dataset CASIA v2.0 prove the potency of the proposed model. It
comfortably beats the existing state-of-the-arts by achieving an IoU score of
0.851, pixel F1 score of 0.9195 and pixel AUC score of 0.8989
Towards Effective Image Forensics via A Novel Computationally Efficient Framework and A New Image Splice Dataset
Splice detection models are the need of the hour since splice manipulations
can be used to mislead, spread rumors and create disharmony in society.
However, there is a severe lack of image splicing datasets, which restricts the
capabilities of deep learning models to extract discriminative features without
overfitting. This manuscript presents two-fold contributions toward splice
detection. Firstly, a novel splice detection dataset is proposed having two
variants. The two variants include spliced samples generated from code and
through manual editing. Spliced images in both variants have corresponding
binary masks to aid localization approaches. Secondly, a novel
Spatio-Compression Lightweight Splice Detection Framework is proposed for
accurate splice detection with minimum computational cost. The proposed
dual-branch framework extracts discriminative spatial features from a
lightweight spatial branch. It uses original resolution compression data to
extract double compression artifacts from the second branch, thereby making it
'information preserving.' Several CNNs are tested in combination with the
proposed framework on a composite dataset of images from the proposed dataset
and the CASIA v2.0 dataset. The best model accuracy of 0.9382 is achieved and
compared with similar state-of-the-art methods, demonstrating the superiority
of the proposed framework
Datasets, Clues and State-of-the-Arts for Multimedia Forensics: An Extensive Review
With the large chunks of social media data being created daily and the
parallel rise of realistic multimedia tampering methods, detecting and
localising tampering in images and videos has become essential. This survey
focusses on approaches for tampering detection in multimedia data using deep
learning models. Specifically, it presents a detailed analysis of benchmark
datasets for malicious manipulation detection that are publicly available. It
also offers a comprehensive list of tampering clues and commonly used deep
learning architectures. Next, it discusses the current state-of-the-art
tampering detection methods, categorizing them into meaningful types such as
deepfake detection methods, splice tampering detection methods, copy-move
tampering detection methods, etc. and discussing their strengths and
weaknesses. Top results achieved on benchmark datasets, comparison of deep
learning approaches against traditional methods and critical insights from the
recent tampering detection methods are also discussed. Lastly, the research
gaps, future direction and conclusion are discussed to provide an in-depth
understanding of the tampering detection research arena
Deep multi-scale discriminative networks for double JPEG compression forensics
© 2019 Association for Computing Machinery. As JPEG is the most widely used image format, the importance of tampering detection for JPEG images in blind forensics is self-evident. In this area, extracting effective statistical characteristics from a JPEG image for classification remains a challenge. Effective features are designed manually in traditional methods, suggesting that extensive labor-consuming research and derivation is required. In this article, we propose a novel image tampering detection method based on deep multi-scale discriminative networks (MSD-Nets). The multi-scale module is designed to automatically extract multiple features from the discrete cosine transform (DCT) coefficient histograms of the JPEG image. This module can capture the characteristic information in different scale spaces. In addition, a discriminative module is also utilized to improve the detection effect of the networks in those difficult situations when the first compression quality (QF1) is higher than the second one (QF2). A special network in this module is designed to distinguish the small statistical difference between authentic and tampered regions in these cases. Finally, a probability map can be obtained and the specific tampering area is located using the last classification results. Extensive experiments demonstrate the superiority of our proposed method in both quantitative and qualitative metrics when compared with state-of-the-art approaches