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adversarial-attacks-pytorch

PyTorch implementation of adversarial attacks.

copy-move-forgery-detection-image-blobs_akaze-orb-brisk-surf-sift-results-on-miccf220-miccf2000

Copy-Move forgery detection results on MICC-F220 dataset using Image blobs and features: AKAZE, ORB, BRISK, SURF and SIFT

copy-move-forgery-detection-using-dog-and-orb

Copy–Move forgery or Cloning is a type of Image tampering where a part of the image is copied and pasted on another part of same image. Copy–move forgery detection technique using DoG (Difference of Gaussian) blob detector, with rotation invariant and resistant to noise feature called ORB (Oriented Fast and Rotated Brief) is poroposed.

copy-move-forgery-detection-using-image-blobs-brisk-features.

One of the most frequently used types of digital image forgery is copying one area in the image and pasting it into another area of the same image. This is known as the copy-move forgery. In this paper, we present two efficient techniques for Copy-move forgery detection that use image blobs and key-points to tackle the limits of the existing copy-move forgery detection methods. The first method is based on image blobs and BRISK feature. The second method is based on image blobs and AKAZE feature. The two proposed methods utilize the same pipeline, that is image blobs are found in the image being analyzed, then features are extracted in each blob and the matching process between features from different blobs is performed. The two proposed methods are implemented and evaluated on the copy-move forgery standard datasets MICC-F8multi, MICC-F200, and CoMoFoD. Keywords: AKAZE, BRISK, Blob, CMFD, DoG, LoG

covid_app_streamlit

covid_app_streamlit

geometric-transformations-parameters-estimation-from-copy-move-forgery-using-image-blobs-and-keypoin

Abstract A copy-move forgery is a passive tampering wherein one or more regions have been copied and pasted within the same image. Often, geometric transformations, including scale, rotation, and rotation+scale are applied to the forged areas to conceal the counterfeits to the copy-move forgery detection methods. Recently, copy-move forgery detection using image blobs have been used to tackle the limitation of the existing detection methods. However, the main limitation of blobs-based copy-move forgery detection methods is the inability to perform the geometric transformation estimation. To tackle the above-mentioned limitation, this article presents a technique that detects copy-move forgery and estimates the geometric transformation parameters between the authentic region and its duplicate using image blobs and scale-rotation invariant keypoints. The proposed algorithm involves the following steps: image blobs are found in the image being analyzed; scale-rotation invariant features are extracted; the keypoints that are located within the same blob are identified; feature matching is performed between keypoints that are located within different blobs to find similar features; finally, the blobs with matched keypoints are post-processed and a 2D affine transformations is computed to estimate the geometric transformation parameters. Our technique is flexible and can easily take in various scale-rotation invariant keypoints including AKAZE, ORB, BRISK, SURF, and SIFT to enhance the effectiveness. The proposed algorithm is implemented and evaluated on images forged with copy-move regions combined with geometric transformation from standard datasets. The experimental results indicate that the new algorithm is effective for geometric transformation parameters estimation.

gukoresha_mtn_momo_api_na_python_django

image-splicing-detection-technique-based-on-illumination-reflectance-model-and-lbp

Image Splicing Forgery Detection using Illumination-Reflectance model

leaf_desease_image_diagnosis

Leaf desease image diagnosis

medical_image_diagnosis

medical_image_diagnosis

medicaldeseasedetection

phd_thesis_synopsis

python_course

ranknet

Learning to Rank from Pair-wise data

swin-transformer

This is an official implementation for "Swin Transformer: Hierarchical Vision Transformer using Shifted Windows".

swin-transformer-semantic-segmentation

This is an official implementation for "Swin Transformer: Hierarchical Vision Transformer using Shifted Windows" on Semantic Segmentation.

test

using-illumination-and-lbp-on-covid-ct-dataset-covid-19-

Using Illumination, LBP and Machine Learning techniques on COVID-CT-Dataset( COVID-19):