Seam Carving Detection and Localization using Two-Stage Deep Neural Networks.

In this paper, we propose a two-step method to detect and localize seam carved images. First, we build a detector to detect small patches in an image that has been seam carved. ... Our experimental results show that our approach is effective in detecting and localizing seam carved images. ... In this paper, we propose a novel method to detect and localize seam carved images using two stages of convolutional neural networks (CNNs): one for detection and one for localization. ...

doi:10.1007/978-981-16-0289-4_29 fatcat:6i755bgvzvharigj7fekgcu2bu

Multiple Versions

We propose a method for creating a deep energy map using an encoder-decoder convolution neural network. A deep energy map preserves important parts or boundaries in an image, without distortion. ... Seam carving uses the energy map from an image. It also removes a seam where the energy is the minimum. ... APPENDIX FOR ''CONTENT-GUIDED SEAM CARVING USING DEEP CONVOLUTION NEURAL NETWORK'' See Figure 7. ...

doi:10.1109/access.2018.2885347 fatcat:jy4kzkda7rflflpy6sg5vmyqoa

DOAJ

While there are methods to detect seam carving based manipulations, this is the first time that robust localization and detection of seam carving forgery is made possible. ... This paper proposes a novel approach for detecting and localizing seams in such images. ... Acknowledgment This material is based upon work supported by the Defense Advanced Research Projects Agency (DARPA), the National Geospatial-Intelligence Agency (NGA) and the ...

arXiv:2108.12534v1 fatcat:xtwvj3r4gzaodczivus4a5ttde

Open Access

In this paper, we propose a convolutional neural network (CNN)-based approach to classifying seam-carving-based image retargeting for reduction and expansion. ... Seam carving is actively exploited to overcome diversity in the resolution of images between applications and devices; hence, detecting the distortion caused by seam carving has become important in image ... COMPARATIVE CONVOLUTIONAL NEURAL NETWORKS FOR THREE-CLASS CLASSIFICATION ON VARIOUS RETARGETING RATIOS (%).TABLE II PERFORMANCE EVALUATION OF ILFNET AND COMPARATIVE CONVOLUTIONAL NEURAL NETWORKS FOR TWO-CLASS ...

arXiv:2007.02393v2 fatcat:c4qtdak6rnbavd5wn2tkgbbm4m

Multiple Versions

We present a neural network to estimate the visual information of important pixels in image and video, which is used in content-aware media retargeting applications. ... Yet, the serious distortion and the shrunk problem in the retargeted results still need to be investigated due to the limitations in the methods used to analyze visual attention. ... Instead of using gradient based to compute the energy map, they propose a method for creating a deep energy map using an encoder-decoder convolution neural network. ...

arXiv:2111.04396v1 fatcat:6wthpdf45zh4dgv2u3y7annoqy

Open Access

Initially, the related problems have been read and analyzed to learn about the techniques used in the past and to be updated with the current works. We implemented Seam Carving and LZW algorithms. ... We got a compression rate of 1.82:1 with 13.75s average time for each file and a decompression rate of 1.64:1 and 7.5 s average time for each file. ... ACKNOWLEDGEMENTS This work was possible thanks to Universidad EAFIT, the government and their scholarships which allowed us to be here finishing this investigation started at the beginning of the semester ...

arXiv:2112.01251v1 fatcat:uw2bkgy2fnalzhbsikqu5dqmii

Open Access

Moreover, artificial neural network when combined with pattern recognition techniques, such ... and other electronic gadgets, has dramatically changed the way we connect with the world around us. ... Acknowledgments We would like to express our appreciation to all the authors for their informative contributions and the reviewers for their support and constructive critiques in making this special issue ...

doi:10.1007/s11042-020-08672-5 fatcat:dmusbepcancb5i6jqo7hhf6a2m

We propose two different neural network architectures - (a) FANnet to achieve structural consistency with source font and (b) Colornet to preserve source color. ... We approach the problem in two stages. At first, the unobserved character (target) is generated from an observed character (source) being modified. ... Supervised [31] and definite samples [4] of observations are used to generate the unknown samples using deep neural architecture. ...

arXiv:1903.01192v2 fatcat:5l52wwrauzayzal6xcotsctcma

Multiple Versions

Further, characters are recognized using a three layer Convolutional Neural Network and the recognition accuracy (Ar) is found to be 73% approximately, the recognized images are further converted into ... The binarized images are being further segmented using Seam Carbel method at character level and are manually compared with the vocabulary, the segmentation accuracy (A s) comes out to be 70% approximately ... Firstly, various neural networks are used like CNN, LSTM and RNN, secondly it focuses on word or text image of different length using both one and two dimensional RNN and error rate drops to 0.42. ...

doi:10.37624/ijert/13.9.2020.2177-2184 fatcat:odcw7hwapvg2netvjc74be2w5y

In this paper, we present a novel method to map the salient region of a hyperbola using an asymmetry measurement and seam carving. ... Second, in order to make the hyperbolic region stand out, we improve the calculation of seam carving to extract the saliency map with the hyperbolic region. ... Acknowledgements The authors thank the editor and anonymous reviewers for their helpful comments and valuable suggestions. Availability of data and materials We can provide the data. ...

doi:10.1186/s13640-018-0296-4 fatcat:z2e5cslckvemxne3gbdyartfge

DOAJ

A CNN (Convolutional Neural Network) precedes the BLST-CTC to extract the features and to feed the BLSTM with the essential of the text-line image. ... In this work, we propose an effective segmentation of Arabic handwritten text into text-lines and words, using deep learning. ... Then, they compute separating seam, using a modified version of seam carving procedure [5] . ...

doi:10.1109/icdarw.2019.50110 dblp:conf/icdar/NecheBE19 fatcat:g7gtjcqawjcllog263g6npqblu

After reviewing literature from researchers it is suggested that the use of the single operator in image retargeting such as scaling, cropping, seam carving, and warping is not sufficient for obtaining ... Nowadays, many efficient content-aware image resizing techniques are being used to safeguard the prominent regions and to generate better results that are visually appealing and pleasing while resizing ... In recent years, due to the ability to extract the high semantic information Convolutional Neural Network (CNN) fascinate much attention and it has shown satisfactory results. ...

doi:10.3837/tiis.2020.07.015 fatcat:qm2yfnvqzrf5ld6vgt46z2mg3a

In this paper, we propose two methods to detect and localize image manipulations based on a combination of resampling features and deep learning. ... Deep learning classifiers and a Gaussian conditional random field model are then used to create a heatmap. Tampered regions are located using a Random Walker segmentation method. ... Figure 3 : 3 Deep Neural networks for detecting resampling in small patches. ...

doi:10.1109/cvprw.2017.235 dblp:conf/cvpr/BunkBMNFMCRP17 fatcat:twryuybkanbmvplqiqm7kfmrki

Citation

Jason Bunk, Jawadul H. Bappy, Tajuddin Manhar Mohammed, Lakshmanan Nataraj, Arjuna Flenner, B.S. Manjunath, Shivkumar Chandrasekaran, Amit K. Roy-Chowdhury, Lawrence Peterson. "Detection and Localization of Image Forgeries Using Resampling Features and Deep Learning." 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) (2017) 1881-1889

In this paper, we propose two methods to detect and localize image manipulations based on a combination of resampling features and deep learning. ... Deep learning classifiers and a Gaussian conditional random field model are then used to create a heatmap. Tampered regions are located using a Random Walker segmentation method. ... The views, opinions and/or findings expressed are those of the author and should not be interpreted as representing the official views or policies of the Department of Defense or the U.S. Government. ...

arXiv:1707.00433v1 fatcat:ztjjc3ompbhjrbnt4xo7l5ktfm

In the article ''CarvingNet: Content-guided seam carving using deep convolution neural network,'' by Song et al., the authors propose an improved content-aware image resizing method that uses deep learning ... The proposed method is extended from seam carving, which is another image resizing method. Seam carving uses the energy map from an image. It also removes a seam where the energy is at the minimum. ... The authors use the single-shot refinement neural network (RefineDet) as a base network, which employs top-down architecture to offer contextual information, achieving accurate detection. ...

doi:10.1109/access.2020.3035461 fatcat:rt7ejtponrfexigie4cfpt7gd4

DOAJ

Seam Carving Detection and Localization Using Two-Stage Deep Neural Networks [chapter]

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CarvingNet: Content-Guided Seam Carving Using Deep Convolution Neural Network

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SeeTheSeams: Localized Detection of Seam Carving based Image Forgery in Satellite Imagery [article]

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Deep Convolutional Neural Network for Identifying Seam-Carving Forgery [article]

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Content-aware media retargeting based on deep importance map [article]

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Health Detection on Cattle Compressed Images in Precision Livestock Farming [article]

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Special issue on video and imaging systems for critical engineering applications [SI 1096]

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STEFANN: Scene Text Editor using Font Adaptive Neural Network [article]

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Ancient Text Character Recognition Using Deep Learning

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Salient map of hyperbolas in GPR images

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Arabic Handwritten Documents Segmentation into Text-Lines and Words using Deep Learning

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A Survey on Content Aware Image Resizing Methods

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Detection and Localization of Image Forgeries Using Resampling Features and Deep Learning

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Detection and Localization of Image Forgeries using Resampling Features and Deep Learning [article]

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IEEE Access Special Section Editorial: AI-Driven Big Data Processing: Theory, Methodology, and Applications

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