A Modified Fourier-Mellin Approach for Source Device Identification on
Stabilized Videos
- URL: http://arxiv.org/abs/2005.09984v1
- Date: Wed, 20 May 2020 12:06:40 GMT
- Title: A Modified Fourier-Mellin Approach for Source Device Identification on
Stabilized Videos
- Authors: Sara Mandelli, Fabrizio Argenti, Paolo Bestagini, Massimo Iuliani,
Alessandro Piva, Stefano Tubaro
- Abstract summary: multimedia forensic tools usually exploit characteristic noise traces left by the camera sensor on the acquired frames.
This analysis requires that the noise pattern characterizing the camera and the noise pattern extracted from video frames under analysis are geometrically aligned.
We propose to overcome this limitation by searching scaling and rotation parameters in the frequency domain.
- Score: 72.40789387139063
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: To decide whether a digital video has been captured by a given device,
multimedia forensic tools usually exploit characteristic noise traces left by
the camera sensor on the acquired frames. This analysis requires that the noise
pattern characterizing the camera and the noise pattern extracted from video
frames under analysis are geometrically aligned. However, in many practical
scenarios this does not occur, thus a re-alignment or synchronization has to be
performed. Current solutions often require time consuming search of the
realignment transformation parameters. In this paper, we propose to overcome
this limitation by searching scaling and rotation parameters in the frequency
domain. The proposed algorithm tested on real videos from a well-known
state-of-the-art dataset shows promising results.
Related papers
- GPU-accelerated SIFT-aided source identification of stabilized videos [63.084540168532065]
We exploit the parallelization capabilities of Graphics Processing Units (GPUs) in the framework of stabilised frames inversion.
We propose to exploit SIFT features.
to estimate the camera momentum and %to identify less stabilized temporal segments.
Experiments confirm the effectiveness of the proposed approach in reducing the required computational time and improving the source identification accuracy.
arXiv Detail & Related papers (2022-07-29T07:01:31Z) - Towards Interpretable Video Super-Resolution via Alternating
Optimization [115.85296325037565]
We study a practical space-time video super-resolution (STVSR) problem which aims at generating a high-framerate high-resolution sharp video from a low-framerate blurry video.
We propose an interpretable STVSR framework by leveraging both model-based and learning-based methods.
arXiv Detail & Related papers (2022-07-21T21:34:05Z) - Spatial-Temporal Frequency Forgery Clue for Video Forgery Detection in
VIS and NIR Scenario [87.72258480670627]
Existing face forgery detection methods based on frequency domain find that the GAN forged images have obvious grid-like visual artifacts in the frequency spectrum compared to the real images.
This paper proposes a Cosine Transform-based Forgery Clue Augmentation Network (FCAN-DCT) to achieve a more comprehensive spatial-temporal feature representation.
arXiv Detail & Related papers (2022-07-05T09:27:53Z) - Task Agnostic Restoration of Natural Video Dynamics [10.078712109708592]
In many video restoration/translation tasks, image processing operations are na"ively extended to the video domain by processing each frame independently.
We propose a general framework for this task that learns to infer and utilize consistent motion dynamics from inconsistent videos to mitigate the temporal flicker.
The proposed framework produces SOTA results on two benchmark datasets, DAVIS and videvo.net, processed by numerous image processing applications.
arXiv Detail & Related papers (2022-06-08T09:00:31Z) - Video Demoireing with Relation-Based Temporal Consistency [68.20281109859998]
Moire patterns, appearing as color distortions, severely degrade image and video qualities when filming a screen with digital cameras.
We study how to remove such undesirable moire patterns in videos, namely video demoireing.
arXiv Detail & Related papers (2022-04-06T17:45:38Z) - Temporally stable video segmentation without video annotations [6.184270985214255]
We introduce a method to adapt still image segmentation models to video in an unsupervised manner.
We verify that the consistency measure is well correlated with human judgement via a user study.
We observe improvements in the generated segmented videos with minimal loss of accuracy.
arXiv Detail & Related papers (2021-10-17T18:59:11Z) - Cross-Camera Human Motion Transfer by Time Series Analysis [11.454103393879368]
We propose an algorithm that identifies motion seasonality and constructs an additive model to extract transferable patterns.
We improve pose estimation in low-resolution videos by leveraging patterns derived from HR counterparts.
arXiv Detail & Related papers (2021-09-29T03:39:01Z) - Robust Unsupervised Video Anomaly Detection by Multi-Path Frame
Prediction [61.17654438176999]
We propose a novel and robust unsupervised video anomaly detection method by frame prediction with proper design.
Our proposed method obtains the frame-level AUROC score of 88.3% on the CUHK Avenue dataset.
arXiv Detail & Related papers (2020-11-05T11:34:12Z) - Exploring Spatial-Temporal Multi-Frequency Analysis for High-Fidelity
and Temporal-Consistency Video Prediction [12.84409065286371]
We propose a video prediction network based on multi-level wavelet analysis to deal with spatial and temporal information in a unified manner.
Our model shows significant improvements on fidelity and temporal consistency over state-of-the-art works.
arXiv Detail & Related papers (2020-02-23T13:46:29Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.