175 research outputs found
Recent Advances in Digital Image and Video Forensics, Anti-forensics and Counter Anti-forensics
Image and video forensics have recently gained increasing attention due to
the proliferation of manipulated images and videos, especially on social media
platforms, such as Twitter and Instagram, which spread disinformation and fake
news. This survey explores image and video identification and forgery detection
covering both manipulated digital media and generative media. However, media
forgery detection techniques are susceptible to anti-forensics; on the other
hand, such anti-forensics techniques can themselves be detected. We therefore
further cover both anti-forensics and counter anti-forensics techniques in
image and video. Finally, we conclude this survey by highlighting some open
problems in this domain
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
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
Multimedia Forensics
This book is open access. Media forensics has never been more relevant to societal life. Not only media content represents an ever-increasing share of the data traveling on the net and the preferred communications means for most users, it has also become integral part of most innovative applications in the digital information ecosystem that serves various sectors of society, from the entertainment, to journalism, to politics. Undoubtedly, the advances in deep learning and computational imaging contributed significantly to this outcome. The underlying technologies that drive this trend, however, also pose a profound challenge in establishing trust in what we see, hear, and read, and make media content the preferred target of malicious attacks. In this new threat landscape powered by innovative imaging technologies and sophisticated tools, based on autoencoders and generative adversarial networks, this book fills an important gap. It presents a comprehensive review of state-of-the-art forensics capabilities that relate to media attribution, integrity and authenticity verification, and counter forensics. Its content is developed to provide practitioners, researchers, photo and video enthusiasts, and students a holistic view of the field
Forensic Video Analytic Software
Law enforcement officials heavily depend on Forensic Video Analytic (FVA)
Software in their evidence extraction process. However present-day FVA software
are complex, time consuming, equipment dependent and expensive. Developing
countries struggle to gain access to this gateway to a secure haven. The term
forensic pertains the application of scientific methods to the investigation of
crime through post-processing, whereas surveillance is the close monitoring of
real-time feeds.
The principle objective of this Final Year Project was to develop an
efficient and effective FVA Software, addressing the shortcomings through a
stringent and systematic review of scholarly research papers, online databases
and legal documentation. The scope spans multiple object detection, multiple
object tracking, anomaly detection, activity recognition, tampering detection,
general and specific image enhancement and video synopsis.
Methods employed include many machine learning techniques, GPU acceleration
and efficient, integrated architecture development both for real-time and
postprocessing. For this CNN, GMM, multithreading and OpenCV C++ coding were
used. The implications of the proposed methodology would rapidly speed up the
FVA process especially through the novel video synopsis research arena. This
project has resulted in three research outcomes Moving Object Based Collision
Free Video Synopsis, Forensic and Surveillance Analytic Tool Architecture and
Tampering Detection Inter-Frame Forgery.
The results include forensic and surveillance panel outcomes with emphasis on
video synopsis and Sri Lankan context. Principal conclusions include the
optimization and efficient algorithm integration to overcome limitations in
processing power, memory and compromise between real-time performance and
accuracy.Comment: The Forensic Video Analytic Software demo video is available
https://www.youtube.com/watch?v=vsZlYKQxSk
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