Get our free extension to see links to code for papers anywhere online!

Chrome logo Add to Chrome

Firefox logo Add to Firefox

"Topic": models, code, and papers

A Fast Keypoint Based Hybrid Method for Copy Move Forgery Detection

Dec 11, 2016
Sunil Kumar, J. V. Desai, Shaktidev Mukherjee

Copy move forgery detection in digital images has become a very popular research topic in the area of image forensics. Due to the availability of sophisticated image editing tools and ever increasing hardware capabilities, it has become an easy task to manipulate the digital images. Passive forgery detection techniques are more relevant as they can be applied without the prior information about the image in question. Block based techniques are used to detect copy move forgery, but have limitations of large time complexity and sensitivity against affine operations like rotation and scaling. Keypoint based approaches are used to detect forgery in large images where the possibility of significant post processing operations like rotation and scaling is more. A hybrid approach is proposed using different methods for keypoint detection and description. Speeded Up Robust Features (SURF) are used to detect the keypoints in the image and Binary Robust Invariant Scalable Keypoints (BRISK) features are used to describe features at these keypoints. The proposed method has performed better than the existing forgery detection method using SURF significantly in terms of detection speed and is invariant to post processing operations like rotation and scaling. The proposed method is also invariant to other commonly applied post processing operations like adding Gaussian noise and JPEG compression


  Access Paper or Ask Questions

Extracting Region of Interest for Palm Print Authentication

Dec 21, 2013
Kasturika B. Ray

Biometrics authentication is an effective method for automatically recognizing individuals. The authentication consists of an enrollment phase and an identification or verification phase. In the stages of enrollment known (training) samples after the pre-processing stage are used for suitable feature extraction to generate the template database. In the verification stage, the test sample is similarly pre processed and subjected to feature extraction modules, and then it is matched with the training feature templates to decide whether it is a genuine or not. This paper presents use of a region of interest (ROI) for palm print technology. First some of the existing methods for palm print identification have been introduced. Then focus has been given on extraction of a suitable smaller region from the acquired palm print to improve the identification method accuracy. Several existing work in the topic of region extraction have been examined. Subsequently, a simple and original method has then proposed for locating the ROI that can be effectively used for palm print analysis. The ROI extracted using this new technique is suitable for different types of processing as it creates a rectangular or square area around the center of activity represented by the lines, wrinkles and ridges of the palm print. The effectiveness of the ROI approach has been tested by integrating it with a texture based identification / authentication system proposed earlier. The improvement has been shown by comparing the identification accuracy rate before and after the ROI pre-processing.

* IJASCSE Journal, Volume 2, Issue 6, December2013 
* 8 pages,4 figures, 1 table, 1 photo of author and 1 photo of co-author, 3 paper has published (2011, 2011, 2012) 

  Access Paper or Ask Questions

An Explicit Nonlinear Mapping for Manifold Learning

Jan 15, 2010
Hong Qiao, Peng Zhang, Di Wang, Bo Zhang

Manifold learning is a hot research topic in the field of computer science and has many applications in the real world. A main drawback of manifold learning methods is, however, that there is no explicit mappings from the input data manifold to the output embedding. This prohibits the application of manifold learning methods in many practical problems such as classification and target detection. Previously, in order to provide explicit mappings for manifold learning methods, many methods have been proposed to get an approximate explicit representation mapping with the assumption that there exists a linear projection between the high-dimensional data samples and their low-dimensional embedding. However, this linearity assumption may be too restrictive. In this paper, an explicit nonlinear mapping is proposed for manifold learning, based on the assumption that there exists a polynomial mapping between the high-dimensional data samples and their low-dimensional representations. As far as we know, this is the first time that an explicit nonlinear mapping for manifold learning is given. In particular, we apply this to the method of Locally Linear Embedding (LLE) and derive an explicit nonlinear manifold learning algorithm, named Neighborhood Preserving Polynomial Embedding (NPPE). Experimental results on both synthetic and real-world data show that the proposed mapping is much more effective in preserving the local neighborhood information and the nonlinear geometry of the high-dimensional data samples than previous work.


  Access Paper or Ask Questions

A Machine Learning Tutorial for Operational Meteorology, Part I: Traditional Machine Learning

Apr 15, 2022
Randy J. Chase, David R. Harrison, Amanda Burke, Gary M. Lackmann, Amy McGovern

Recently, the use of machine learning in meteorology has increased greatly. While many machine learning methods are not new, university classes on machine learning are largely unavailable to meteorology students and are not required to become a meteorologist. The lack of formal instruction has contributed to perception that machine learning methods are 'black boxes' and thus end-users are hesitant to apply the machine learning methods in their every day workflow. To reduce the opaqueness of machine learning methods and lower hesitancy towards machine learning in meteorology, this paper provides a survey of some of the most common machine learning methods. A familiar meteorological example is used to contextualize the machine learning methods while also discussing machine learning topics using plain language. The following machine learning methods are demonstrated: linear regression; logistic regression; decision trees; random forest; gradient boosted decision trees; naive Bayes; and support vector machines. Beyond discussing the different methods, the paper also contains discussions on the general machine learning process as well as best practices to enable readers to apply machine learning to their own datasets. Furthermore, all code (in the form of Jupyter notebooks and Google Colaboratory notebooks) used to make the examples in the paper is provided in an effort to catalyse the use of machine learning in meteorology.


  Access Paper or Ask Questions

ROMNet: Renovate the Old Memories

Feb 05, 2022
Runsheng Xu, Zhengzhong Tu, Yuanqi Du, Xiaoyu Dong, Jinlong Li, Zibo Meng, Jiaqi Ma, Hongkai YU

Renovating the memories in old photos is an intriguing research topic in computer vision fields. These legacy images often suffer from severe and commingled degradations such as cracks, noise, and color-fading, while lack of large-scale paired old photo datasets makes this restoration task very challenging. In this work, we present a novel reference-based end-to-end learning framework that can jointly repair and colorize the degraded legacy pictures. Specifically, the proposed framework consists of three modules: a restoration sub-network for degradation restoration, a similarity sub-network for color histogram matching and transfer, and a colorization subnet that learns to predict the chroma elements of the images conditioned on chromatic reference signals. The whole system takes advantage of the color histogram priors in a given reference image, which vastly reduces the dependency on large-scale training data. Apart from the proposed method, we also create, to our knowledge, the first public and real-world old photo dataset with paired ground truth for evaluating old photo restoration models, wherein each old photo is paired with a manually restored pristine image by PhotoShop experts. Our extensive experiments conducted on both synthetic and real-world datasets demonstrate that our method significantly outperforms state-of-the-arts both quantitatively and qualitatively.

* Submitted to TIP 

  Access Paper or Ask Questions

A probabilistic latent variable model for detecting structure in binary data

Jan 26, 2022
Christopher Warner, Kiersten Ruda, Friedrich T. Sommer

We introduce a novel, probabilistic binary latent variable model to detect noisy or approximate repeats of patterns in sparse binary data. The model is based on the "Noisy-OR model" (Heckerman, 1990), used previously for disease and topic modelling. The model's capability is demonstrated by extracting structure in recordings from retinal neurons, but it can be widely applied to discover and model latent structure in noisy binary data. In the context of spiking neural data, the task is to "explain" spikes of individual neurons in terms of groups of neurons, "Cell Assemblies" (CAs), that often fire together, due to mutual interactions or other causes. The model infers sparse activity in a set of binary latent variables, each describing the activity of a cell assembly. When the latent variable of a cell assembly is active, it reduces the probabilities of neurons belonging to this assembly to be inactive. The conditional probability kernels of the latent components are learned from the data in an expectation maximization scheme, involving inference of latent states and parameter adjustments to the model. We thoroughly validate the model on synthesized spike trains constructed to statistically resemble recorded retinal responses to white noise stimulus and natural movie stimulus in data. We also apply our model to spiking responses recorded in retinal ganglion cells (RGCs) during stimulation with a movie and discuss the found structure.

* 25 pages, 20 figures 

  Access Paper or Ask Questions

PrintsGAN: Synthetic Fingerprint Generator

Jan 10, 2022
Joshua J. Engelsma, Steven A. Grosz, Anil K. Jain

A major impediment to researchers working in the area of fingerprint recognition is the lack of publicly available, large-scale, fingerprint datasets. The publicly available datasets that do exist contain very few identities and impressions per finger. This limits research on a number of topics, including e.g., using deep networks to learn fixed length fingerprint embeddings. Therefore, we propose PrintsGAN, a synthetic fingerprint generator capable of generating unique fingerprints along with multiple impressions for a given fingerprint. Using PrintsGAN, we synthesize a database of 525,000 fingerprints (35,000 distinct fingers, each with 15 impressions). Next, we show the utility of the PrintsGAN generated dataset by training a deep network to extract a fixed-length embedding from a fingerprint. In particular, an embedding model trained on our synthetic fingerprints and fine-tuned on a small number of publicly available real fingerprints (25,000 prints from NIST SD302) obtains a TAR of 87.03% @ FAR=0.01% on the NIST SD4 database (a boost from TAR=73.37% when only trained on NIST SD302). Prevailing synthetic fingerprint generation methods do not enable such performance gains due to i) lack of realism or ii) inability to generate multiple impressions per finger. We plan to release our database of synthetic fingerprints to the public.


  Access Paper or Ask Questions

A comment-driven evidence appraisal approach for decision-making when only uncertain evidence available

Dec 21, 2021
Shuang Wang, Jian Du

Purpose: To explore whether comments could be used as an assistant tool for heuristic decision-making, especially in cases where missing, incomplete, uncertain, or even incorrect evidence is acquired. Methods: Six COVID-19 drug candidates were selected from WHO clinical guidelines. Evidence-comment networks (ECNs) were completed of these six drug candidates based on evidence-comment pairs from all PubMed indexed COVID-19 publications with formal published comments. WHO guidelines were utilized to validate the feasibility of comment-derived evidence assertions as a fast decision supporting tool. Results: Out of 6 drug candidates, comment-derived evidence assertions of leading subgraphs of 5 drugs were consistent with WHO guidelines, and the overall comment sentiment of 6 drugs was aligned with WHO clinical guidelines. Additionally, comment topics were in accordance with the concerns of guidelines and evidence appraisal criteria. Furthermore, half of the critical comments emerged 4.5 months earlier than the date guidelines were published. Conclusions: Comment-derived evidence assertions have the potential as an evidence appraisal tool for heuristic decisions based on the accuracy, sensitivity, and efficiency of evidence-comment networks. In essence, comments reflect that academic communities do have a self-screening evaluation and self-purification (argumentation) mechanism, thus providing a tool for decision makers to filter evidence.


  Access Paper or Ask Questions

Assessing a Single Image in Reference-Guided Image Synthesis

Dec 08, 2021
Jiayi Guo, Chaoqun Du, Jiangshan Wang, Huijuan Huang, Pengfei Wan, Gao Huang

Assessing the performance of Generative Adversarial Networks (GANs) has been an important topic due to its practical significance. Although several evaluation metrics have been proposed, they generally assess the quality of the whole generated image distribution. For Reference-guided Image Synthesis (RIS) tasks, i.e., rendering a source image in the style of another reference image, where assessing the quality of a single generated image is crucial, these metrics are not applicable. In this paper, we propose a general learning-based framework, Reference-guided Image Synthesis Assessment (RISA) to quantitatively evaluate the quality of a single generated image. Notably, the training of RISA does not require human annotations. In specific, the training data for RISA are acquired by the intermediate models from the training procedure in RIS, and weakly annotated by the number of models' iterations, based on the positive correlation between image quality and iterations. As this annotation is too coarse as a supervision signal, we introduce two techniques: 1) a pixel-wise interpolation scheme to refine the coarse labels, and 2) multiple binary classifiers to replace a na\"ive regressor. In addition, an unsupervised contrastive loss is introduced to effectively capture the style similarity between a generated image and its reference image. Empirical results on various datasets demonstrate that RISA is highly consistent with human preference and transfers well across models.

* Accepted by AAAI 2022 

  Access Paper or Ask Questions

A comment-derived evidence appraisal approach for decision-making using uncertain evidence

Dec 05, 2021
Shuang Wang, Jian Du

Purpose: To explore whether comments could be used as an assistant tool for heuristic decision-making, especially in cases where missing, incomplete, uncertain, or even incorrect evidence is acquired. Methods: Six COVID-19 drug candidates were selected from WHO clinical guidelines. Evidence-comment networks (ECNs) were completed of these six drug candidates based on evidence-comment pairs from all PubMed indexed COVID-19 publications with formal published comments. WHO guidelines were utilized to validate the feasibility of comment-derived evidence assertions as a fast decision supporting tool. Results: Out of 6 drug candidates, comment-derived evidence assertions of leading subgraphs of 5 drugs were consistent with WHO guidelines, and the overall comment sentiment of 6 drugs was aligned with WHO clinical guidelines. Additionally, comment topics were in accordance with the concerns of guidelines and evidence appraisal criteria. Furthermore, half of the critical comments emerged 4.5 months earlier than the date guidelines were published. Conclusions: Comment-derived evidence assertions have the potential as an evidence appraisal tool for heuristic decisions based on the accuracy, sensitivity, and efficiency of evidence-comment networks. In essence, comments reflect that academic communities do have a self-screening evaluation and self-purification (argumentation) mechanism, thus providing a tool for decision makers to filter evidence.


  Access Paper or Ask Questions

<<
526
527
528
529
530
531
532
533
534
535
536
537
538
>>