Search Results for "image forgery detection" - Page 4
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Showing 94 open source projects for "image forgery detection"
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DeepCluster
Deep Clustering for Unsupervised Learning of Visual Features
DeepCluster is a classic self-supervised clustering-based representation learning algorithm that iteratively groups image features and uses the cluster assignments as pseudo-labels to train the network. In each round, features produced by the network are clustered (e.g. k-means), and the cluster IDs become supervision targets in the next epoch, encouraging the model to refine its representation to better separate semantic groups. This alternating “cluster & train” scheme helps the model... -
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COCO Annotator
Web-based image segmentation tool for object detection & localization
COCO Annotator is a web-based image annotation tool designed for versatility and efficiently label images to create training data for image localization and object detection. It provides many distinct features including the ability to label an image segment (or part of a segment), track object instances, label objects with disconnected visible parts, and efficiently store and export annotations in the well-known COCO format. -
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Tensor2Tensor
Library of deep learning models and datasets
Deep Learning (DL) has enabled the rapid advancement of many useful technologies, such as machine translation, speech recognition and object detection. In the research community, one can find code open-sourced by the authors to help in replicating their results and further advancing deep learning. However, most of these DL systems use unique setups that require significant engineering effort and may only work for a specific problem or architecture, making it hard to run new experiments and... -
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Tensorflow and deep learning
A crash course in six episodes for software developers
Tensorflow and deep learning repository is an educational deep learning crash course designed to help software developers quickly understand and apply machine learning concepts without requiring advanced academic background. It is structured as a series of guided lessons that combine theoretical explanations, practical examples, and runnable code, allowing learners to build intuition while actively experimenting with models. The repository covers core neural network concepts such as weights,... -
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DeepFaceLab
The leading software for creating deepfakes
...It offers an imperative and easy-to-use pipeline that even those without a comprehensive understanding of the deep learning framework or model implementation can use; and yet also provides a flexible and loose coupling structure for those who want to strengthen their own pipeline with other features without having to write complicated boilerplate code. DeepFaceLab can achieve results with high fidelity that are indiscernible by mainstream forgery detection approaches. Apart from seamlessly swapping faces, it can also de-age faces, replace the entire head, and even manipulate speech (though this will require some skill in video editing). -
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TensorNets
High level network definitions with pre-trained weights in TensorFlow
High level network definitions with pre-trained weights in TensorFlow (tested with 2.1.0 >= TF >= 1.4.0). Applicability. Many people already have their own ML workflows and want to put a new model on their workflows. TensorNets can be easily plugged together because it is designed as simple functional interfaces without custom classes. Manageability. Models are written in tf.contrib.layers, which is lightweight like PyTorch and Keras, and allows for ease of accessibility to every weight and... -
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TensorFlow Object Counting API
The TensorFlow Object Counting API is an open source framework
The TensorFlow Object Counting API is an open source framework built on top of TensorFlow and Keras that makes it easy to develop object counting systems. Please contact if you need professional object detection & tracking & counting project with super high accuracy and reliability! You can train TensorFlow models with your own training data to built your own custom object counter system! If you want to learn how to do it, please check one of the sample projects, which cover some of the... -
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xsrfprobe
Advanced toolkit for detecting and exploiting CSRF vulnerabilities
XSRFProbe is an advanced security auditing toolkit designed to detect and analyze Cross Site Request Forgery (CSRF/XSRF) vulnerabilities in web applications. It uses an automated crawling engine that continuously scans a target application, collects forms and endpoints, and evaluates them for potential CSRF weaknesses. XSRFProbe performs numerous systematic checks to determine whether a web endpoint is vulnerable, including inspection of anti-CSRF tokens, cookie validation behavior, and... -
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VGGFace2
VGGFace2 Dataset for Face Recognition
...These models achieve strong verification performance on benchmarks such as IJB-B and include variants with lower-dimensional embeddings for compact feature representation. The project also includes preprocessing tools, face detection scripts, and etc. -
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ChainerCV
ChainerCV: a Library for Deep Learning in Computer Vision
...Dataset classes whose names end with BboxDataset contain annotations of where objects locate in an image and which categories they are assigned to. These datasets can be indexed to return a tuple of an image, bounding boxes and labels. ChainerCV provides several network implementations that carry out object detection. -
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ARK AR Sticker Generator
ARK is an AR Sticker Generator
In Augmented Reality Development we often need high quality markers for image targets compatible with accurate detection. ARK is an AR sticker generator made using various image processing libraries & scientific tools. It creates a colorful sticker similar to a QR code with each sticker being completely unique from other. It also allows to add an image overlay for branding or other stuff. Generated stickers can be found in Output folder. -
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DIGITS
Deep Learning GPU training system
The NVIDIA Deep Learning GPU Training System (DIGITS) puts the power of deep learning into the hands of engineers and data scientists. DIGITS can be used to rapidly train the highly accurate deep neural network (DNNs) for image classification, segmentation and object detection tasks. DIGITS simplifies common deep learning tasks such as managing data, designing and training neural networks on multi-GPU systems, monitoring performance in real-time with advanced visualizations, and selecting the best performing model from the results browser for deployment. ... -
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PyCNN
Image Processing with Cellular Neural Networks in Python
Image Processing with Cellular Neural Networks in Python. Cellular Neural Networks (CNN) are a parallel computing paradigm that was first proposed in 1988. Cellular neural networks are similar to neural networks, with the difference that communication is allowed only between neighboring units. Image Processing is one of its applications. CNN processors were designed to perform image processing; specifically, the original application of CNN processors was to perform real-time ultra-high... -
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RadicalSpam Virtual Appliance
Virtual Appliance of RadicalSpam
RadicalSpam Virtual Appliance takes full solution of RadicalSpam Community Edition , pre-installed in a OVF virtual machine ( Open Virtual Format ) compatible with the best virtualization platforms on the market , including VMware ESX Server. More information : http://www.radical-spam.org -
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IMAGINE
Biological image viewer and processor
Detection, enumeration, and sizing of biological organisms by image analysis. -
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colonyzer image analysis software
Image analysis estimating cell density in arrayed microbial cultures
Image analysis software for quantification of cell density in microbial cultures (e.g. S. cerevisiae) growing on solid agar plates. Specialises in detection of extremely low cell densities. Forms part of the Quantitative Fitness Analysis (QFA) workflow: http://research.ncl.ac.uk/qfa/ Suitable for high-throughput, genome-wide analysis of culture libraries when combined with the following qfa R package: http://qfa.r-forge.r-project.org/ This is the version presented, used and demonstrated in the following manuscripts: Lawless et al. 2010 http://dx.doi.org/10.1186/1471-2105-11-287 Addinall et al. 2011 http://dx.doi.org/10.1371/journal.pgen.1001362 Chang et al. 2011 http://dx.doi.org/10.1534/g3.111.000216 Banks et al. 2012 http://dx.doi.org/10.3791/4018 Development has been moved to github, where Colonyzer has undergone several recent improvements, particuarly making installation easier and analysis faster: https://github.com/CnrLwlss/Colonyzer -
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Monk Computer Vision
A low code unified framework for computer vision and deep learning
...A Unified wrapper over major deep learning frameworks. Our core focus area is at the intersection of Computer Vision and Deep Learning algorithms. - Monk Object Detection - https://github.com/Tessellate-Imaging/Monk_Object_Detection. Monk object detection is our take on assembling state of the art object detection, image segmentation, pose estimation algorithms at one place, making them low code and easily configurable on any machine. - Monk GUI - https://github.com/Tessellate-Imaging/Monk_Gui. ...
