121 research outputs found
Deep Convolutional Neural Network for Object Forgery Detection in Video
No full textMaster of Engineering- ECTalking of today’s digital revolution, where visual data is playing an imperative role, accessing,
processing, and sharing of most of the information is typically attained with the help of video.
These video sequences have shown their significance in various fields like news broadcasting,
legal trials in court rooms, and many more but the doctoring of authentic visual content has
made it uncertain to use as an evidence. Doctored video generation with a fast-growing rate
done by easily accessible editing software like Adobe Photoshop, filmora, etc. have proved to
be a major problem in maintaining its authenticity. The extent of forging is so vast that video
spoofs reach our electronic-mail in-boxes, WhatsApp, Facebook or any other social media
every minute and this fakery is totally indistinguishable that hence raise a demand for a new
versatile field to perceive any alteration. Video forgery detection aims at restoring the trust and
validating the authenticity by uncovering the counterfeits. But the traditional approaches used
so far to detect forgeries have faced difficulties like less accurate detection rate and more false
negatives. Nowadays, deep neural networks have been recognized as an effective technique in
eradicating such troubles by learning significant features. The increasing attempt of video
modification has drawn greater attention towards Deep Convolutional Neural Networks
(DCNN) for achieving better counterfeits recognition.The proposed work is about “Deep Convolutional Neural Network for Object Forgery
Detection in Video” that aims to detect forgery without requiring additional pre-embedded
information of the frame. The proposed DCNN consists of various neurons where weights and
biases are defined for individual neuron which helps the network to learn the data properly.
Unlike other pre-existing learning-techniques, the proposed algorithm classifies the forged
frames on the basis of correlation among them and the observed abnormalities using DCNN.
The decoders used for batch normalization of input improves the training swiftness. It leads to
an inordinate evidence in recognizing and discovering the fake regions. Simulation results are
obtained on MATLAB 2018a with NVIDIA Cuda Graphics with REWIND and GRIP dataset
which is rich in video inter-frame forgery effects. The outcomes so obtained with an average
accuracy of 99% shows the superiority of the proposed algorithm as compared to existing one.
The robustness of proposed algorithm is also tested on You Tube compressed video sequences.
Recurrent Neural Networks can be combined with DCNN to achieve comparatively remarkable
results in future.TIE
Image Forensic Using Machine Learning
No full textPh.D ThesisNowadays, it is challenging to trust any digital image due to the convenient availability of
manipulation software like Photoshop, GIMP, and Coral Draw etc. Therefore, it becomes tough to
differentiate between an authentic image and tampered image. Traditional methods for image
forgery detection generally use handcrafted features. The challenge with the traditional image
tampering detection approaches is that most of the methods need improvement as only certain
features are identified. These days, Machine learning (ML) and deep learning (DL) are widely
used in image forgery. These techniques prove their efficacy with better accuracy and other
performance parameters than traditional methods. There are many types of image forgery, like
copy-move, splicing, and retouching. In this thesis, copy-move and splicing forgery are detected
using ML and DL techniques.The first algorithm provides a copy-move image forgery detection using machine learning and
deep learning. In this work, machine and deep learning algorithms are proposed to find out
different image forgeries. First, the proposed algorithm applies color illumination in preprocessing,
then Scale Invarient Feature Transform (SIFT) is used to extract features, and Support
Vector Machine (SVM) classifies correct forged pixels. The proposed methodology gives better
results for CMF detection as Precision=97.25%, Recall=100%, and F1=98.53%.The second algorithm provides a deep convolution neural network (DCNN) that uses automatic
feature extraction and localizes copy-move forgery and splicing forgery. In the feature extraction
and localize forgery, the performance can be enhanced using the ML and DL. Finally, the
applications of proposed color illumination, convolution neural network, and semantic
segmentation are demonstrated for forgery detection. The proposed algorithm performance
accuracy is calculated on the CASIA v1.0 validation set, and the test set is 98% and 99%,
respectively. The performance accuracy is calculated on the CASIA v2.0 validation set, and the
test set is 98% and 98%, respectively. The DVMM dataset forgery detection accuracy is 97%. The
BSDS300 dataset forgery detection accuracy is 98%. The proposed algorithm is tested on imagelevel
on CMFD dataset and achieved performance accuracy, i.e. Precision (P) = 98%, Recall (R)
= 100% and F1 = 99%.The third algorithm presented robustness of algorithms against geometrical attacks using color
illumination, a deep convolution neural network, SIFT, and SVM. Geometrical attacks, such as
scaling, rotation, and JPEG, were identified. The plain CMF attack detection results are:
P=97.25%; R=100% and F1=98.53%. The JPEG CMF attack detection results are: P=71.44%;
R=58.44% and F1=63.77%. The scale CMF attack detection results are: P=85.2%; R=74.8% and
F1=79.1%. The rotation CMF attack results are: P=87.83%; R=76.33% and F1=86.16%.
Comparison with state-of-the-art techniques proves the efficacy of the presented algorithms. In the
future, suggested algorithms can be implemented on real-time applications with some
improvements
An Adaptive Hybrid Algorithm For Digital Image Copy-Move Forgery Detection
No full textDue to the development of sophisticated cameras and image editing tools, digital image
tampering techniques are frequently used without leaving visual cues behind. Digital
image copy-move forgery is said to be an image manipulation which involves copying
and pasting of certain section (or sections) within the same digital image. Generally, this
is done with intention of hiding important information or providing false information in
an image. This motivates a need for forgery detection systems that are transparent to such
manipulations and can disclose whether a given image has been morphed just by
investigating the dummy image.
Several methods have been presented for the copy-move forgery detection in recent
years. Nearly all of the existing block-based methods are computationally expensive and
robust to noise addition, JPEG compression, but are susceptible to geometrical attacks
like rotation, translation and scaling. On the other hand, keypoint-based detection
techniques are computationally efficient as well as perform better under geometrical
attacks in comparison with block-based methods but suffer from low recall rate. The
proposed technique is a hybrid one which incorporates both block-based and keypointbased
schemes in order to deal with their drawbacks. Focus of the proposed thesis work is
on achieving 100% precision and recall at image level copy move forgery detection using
adaptive algorithm. Firstly, adaptive image segmentation is performed on the test image
resulting in image patches followed by detection and extraction of features of these
patches. These features are matched patch-wise to obtain suspected keypoint pairs. An
adaptive keypoint matching algorithm is used to extract matched keypoint pairs from the
suspected keypoint pairs. Finally, an adaptive forgery region extraction is used to locate
similar areas in the test image.
The evaluation results demonstrate that the proposed hybrid scheme is more robust under
plain as well as various challenging situations such as down-sampling, up-scaling, downscaling
and JPEG compression than the prior state-of-the-art techniques. The proposed
scheme achieved improved results with 100% precision, 100% recall and 100% F1 score
at image level, while 95.01% precision, 87.18% recall and 90.92% F1 score at pixel level
under plain copy-move attack. The proposed adaptive scheme can be extended to videos
in future
Tea Tales – India’s ever evolving chai culture
No full textAs we observed International Tea Day on May 21, to peek into the vibrant history of chai and chai tapris in India, Village Square spoke to Arup K Chatterjee, professor of English at OP Jindal Global University. He is the author of widely acclaimed books including, The Purveyors of Destiny: A Cultural Biography of the Indian Railways and The Great Indian Railways
The largest topological ring of functions endowed with the m-topology
No full text[EN] The purpose of this article is to identify the largest subring of the ring of all real valued functions on a Tychonoff space X, which forms a topological ring endowed with the m-topology.The second author acknowledges the support of NBHM Research Grant 02011/6/2020/NBHM(R.P) R&D II/6277.Chauhan, TK.; Jindal, V. (2022). The largest topological ring of functions endowed with the m-topology. Applied General Topology. 23(2):281-286. https://doi.org/10.4995/agt.2022.17080OJS281286232F. Azarpanah, F. Manshoor and R. Mohamadian, Connectedness and compactness in C(X) with m-topology and generalized m-topology, Topol. Appl. 159 (2012), 3486-3493. https://doi.org/10.1016/j.topol.2012.08.010F. Azarpanah, M. Paimann and A. R. Salehi, Connectedness of some rings of quotients of C(X) with them-topology, Comment. Math. Univ. Carolin. 56 (2015), 63-76. https://doi.org/10.14712/1213-7243.015.106L. Gillman and M. Jerison, Rings of continuous functions, Springer-Verlag, New York,1976. Reprint of the 1960 edition, Graduate Texts in Mathematics, No. 43. https://doi.org/10.1007/978-1-4615-7819-2J. Gomez-Prez and W. W. McGovern, Them-topology on C m(X) revisited, Topol. Appl. 153, no. 11 (2006), 1838-1848. https://doi.org/10.1016/j.topol.2005.06.016E. Hewitt, Rings of real-valued continuous functions. I, Trans. Amer. Math. Soc. 64(1948), 45-99. https://doi.org/10.1090/S0002-9947-1948-0026239-9L. Hola and V. Jindal, On graph and fine topologies, Topol. Proc. 49 (2017), 65-73.L. Hola and R. A. McCoy, Compactness in the fine and related topologies, Topol. Appl.109 (2001), 183-190. https://doi.org/10.1016/S0166-8641(99)00160-1L. Hola and B. Novotny, Topology of uniform convergence and m-topology on C(X), Mediterr. J. Math. 14 (2017): 70. https://doi.org/10.1007/s00009-017-0861-6J. G. Horne, Countable paracompactness and cb-spaces, Notices. Amer. Math. Soc. 6 (1959), 629-630.J. Mack, On a class of countably paracompact spaces, Proc. Amer. Math. Soc. 16 (1965),467-472. https://doi.org/10.1090/S0002-9939-1965-0177388-1G. Di Maio, L. Hola, D. Holy and R. A. McCoy, Topologies on the space of continuous functions, Topol. Appl. 86 (1998), 105-122. https://doi.org/10.1016/S0166-8641(97)00114-4R. A. McCoy, Fine topology on function spaces, Int. J. Math. Math. Sci. 9 (1986),417-424. https://doi.org/10.1155/S0161171286000534R. A. McCoy, S. Kundu and V. Jindal, Function spaces with uniform, fine and graphtopologies, Springer Briefs in Mathematics, Springer, Cham, 2018. https://doi.org/10.1007/978-3-319-77054-
Machine Learning Based Saliency Algorithm For Image Forgery Classification And Localization
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