المؤلفون: Moysiadis, Christos, Karantaidis, Georgios, Kotropoulos, Constantine

مصطلحات موضوعية: Computer Science - Computer Vision and Pattern Recognition, Computer Science - Multimedia

الوصف: Electric Network Frequency (ENF) acts as a fingerprint in multimedia forensics applications. In indoor environments, ENF variations affect the intensity of light sources connected to power mains. Accordingly, the light intensity variations captured by sensing devices can be exploited to estimate the ENF. A first optical sensing device based on a photodiode is developed for capturing ENF variations in indoor lighting environments. In addition, a device that captures the ENF directly from power mains is implemented. This device serves as a ground truth ENF collector. Video recordings captured by a camera are also employed to estimate the ENF. The camera serves as a second optical sensor. The factors affecting the ENF estimation are thoroughly studied. The maximum correlation coefficient between the ENF estimated by the two optical sensors and that estimated directly from power mains is used to measure the estimation accuracy. The paper's major contribution is in the disclosure of extensive experimental evidence on ENF estimation in scenes ranging from static ones capturing a white wall to non-static ones, including human activity.

URL الوصول: http://arxiv.org/abs/2311.13954

المؤلفون: Sarridis, Ioannis, Karantaidis, Georgios, Kotropoulos, Constantine

المصدر: 2022 IEEE 14th Image, Video, and Multidimensional Signal Processing Workshop (IVMSP) Image, Video, and Multidimensional Signal Processing Workshop (IVMSP), 2022 IEEE 14th. :1-5 Jun, 2022

Relation: 2022 IEEE 14th Image, Video, and Multidimensional Signal Processing Workshop (IVMSP)

المؤلفون: Karantaidis, Georgios, Kotropoulos, Constantine

مصطلحات موضوعية: Electrical Engineering and Systems Science - Signal Processing, Computer Science - Computer Vision and Pattern Recognition

الوصف: Electric Network Frequency (ENF) fluctuations constitute a powerful tool in multimedia forensics. An efficient approach for ENF estimation is introduced with temporal windowing based on the filter-bank Capon spectral estimator. A type of Gohberg-Semencul factorization of the model covariance matrix is used due to the Toeplitz structure of the covariance matrix. Moreover, this approach uses, for the first time in the field of ENF, a temporal window, not necessarily the rectangular one, at the stage preceding spectral estimation. Krylov matrices are employed for fast implementation of matrix inversions. The proposed approach outperforms the state-of-the-art methods in ENF estimation, when a short time window of $1$ second is employed in power recordings. In speech recordings, the proposed approach yields highly accurate results with respect to both time complexity and accuracy. Moreover, the impact of different temporal windows is studied. The results show that even the most trivial methods for ENF estimation, such as the Short-Time Fourier Transform, can provide better results than the most recent state-of-the-art methods, when a temporal window is employed. The correlation coefficient is used to measure the ENF estimation accuracy.
Comment: 6 pages, 1 figure, IEEE International Workshop on Machine Learning For Signal Processing (MLSP) 2019

URL الوصول: http://arxiv.org/abs/1908.08813

المؤلفون: Karantaidis, Georgios, Sarridis, Ioannis, Kotropoulos, Constantine

مصطلحات موضوعية: Computer Science - Social and Information Networks, Statistics - Machine Learning

الوصف: The high-order relations between the content in social media sharing platforms are frequently modeled by a hypergraph. Either hypergraph Laplacian matrix or the adjacency matrix is a big matrix. Randomized algorithms are used for low-rank factorizations in order to approximately decompose and eventually invert such big matrices fast. Here, block randomized Singular Value Decomposition (SVD) via subspace iteration is integrated within adaptive hypergraph weight estimation for image tagging, as a first approach. Specifically, creating low-rank submatrices along the main diagonal by tessellation permits fast matrix inversions via randomized SVD. Moreover, a second approach is proposed for solving the linear system in the optimization problem of hypergraph learning by employing the conjugate gradient method. Both proposed approaches achieve high accuracy in image tagging measured by F1 score and succeed to reduce the computational requirements of adaptive hypergraph weight estimation.
Comment: 5 pages, 1 figure, International Conference on Image Processing (ICIP) 2019

URL الوصول: http://arxiv.org/abs/1908.08281

المؤلفون: Karantaidis, Georgios, Sarridis, Ioannis, Kotropoulos, Constantine

المصدر: In Signal Processing: Image Communication September 2021 97

المؤلفون: Karantaidis, Georgios, Kotropoulos, Constantine

المصدر: Information (2078-2489); Mar2024, Vol. 15 Issue 3, p158, 23p

مصطلحات موضوعية: ARTIFICIAL neural networks, COGNITIVE styles, SUPERVISED learning, DIGITAL image processing, COMPUTER graphics

مستخلص: The detection of computer-generated (CG) multimedia content has become of utmost importance due to the advances in digital image processing and computer graphics. Realistic CG images could be used for fraudulent purposes due to the deceiving recognition capabilities of human eyes. So, there is a need to deploy algorithmic tools for distinguishing CG images from natural ones within multimedia forensics. Here, an end-to-end framework is proposed to tackle the problem of distinguishing CG images from natural ones by utilizing supervised contrastive learning and arbitrary style transfer by means of a two-stage deep neural network architecture. This architecture enables discrimination by leveraging per-class embeddings and generating multiple training samples to increase model capacity without the need for a vast amount of initial data. Stochastic weight averaging (SWA) is also employed to improve the generalization and stability of the proposed framework. Extensive experiments are conducted to investigate the impact of various noise conditions on the classification accuracy and the proposed framework's generalization ability. The conducted experiments demonstrate superior performance over the existing state-of-the-art methodologies on the public DSTok, Rahmouni, and LSCGB benchmark datasets. Hypothesis testing asserts that the improvements in detection accuracy are statistically significant. [ABSTRACT FROM AUTHOR]

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المؤلفون: Karantaidis, Georgios, Kotropoulos, Constantine

المصدر: Journal of Imaging; Oct2021, Vol. 7 Issue 10, p1-20, 20p

مصطلحات موضوعية: ELECTRIC networks, LUMINANCE (Photometry), DIGITAL video recording, FOURIER transforms, MULTIPLE Signal Classification

مستخلص: Electric Network Frequency (ENF) is embedded in multimedia recordings if the recordings are captured with a device connected to power mains or placed near the power mains. It is exploited as a tool for multimedia authentication. ENF fluctuates stochastically around its nominal frequency at 50/60 Hz. In indoor environments, luminance variations captured by video recordings can also be exploited for ENF estimation. However, the various textures and different levels of shadow and luminance hinder ENF estimation in static and non-static video, making it a non-trivial problem. To address this problem, a novel automated approach is proposed for ENF estimation in static and non-static digital video recordings. The proposed approach is based on the exploitation of areas with similar characteristics in each video frame. These areas, called superpixels, have a mean intensity that exceeds a specific threshold. The performance of the proposed approach is tested on various videos of real-life scenarios that resemble surveillance from security cameras. These videos are of escalating difficulty and span recordings from static ones to recordings, which exhibit continuous motion. The maximum correlation coefficient is employed to measure the accuracy of ENF estimation against the ground truth signal. Experimental results show that the proposed approach improves ENF estimation against the state-of-the-art, yielding statistically significant accuracy improvements. [ABSTRACT FROM AUTHOR]

: Copyright of Journal of Imaging is the property of MDPI and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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المؤلفون: Piretzidis, Dimitrios, Gurveer Sra, Karantaidis, Georgios D., Sideris, Michael G.

URL الوصول: https://explore.openaire.eu/search/publication?articleId=doi_________::95b9c9b4d61a12cb16c83166f20f4e73