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anti-forensics-using-gan

Median Filtered Image Restoration and Anti-Forensics Using Adversarial Networks

application-of-densenet-in-camera-model-identification-and-post-processing-detection

Camera model identification has earned paramount importance in the field of image forensics with an upsurge of digitally altered images which are constantly being shared through websites, media, and social applications. But, the task of identification becomes quite challenging if metadata are absent from the image and/or if the image has been post-processed. In this paper, we present a DenseNet pipeline to solve the problem of identifying the source camera-model of an image. Our approach is to extract patches of 256 x 256 from a labeled image dataset and apply augmentations, i.e., Empirical Mode Decomposition (EMD). We use this extended dataset to train a Neural Network with the DenseNet-201 architecture. We concatenate the output features for 3 different sizes (64x64, 128x128, 256x256) and pass them to a secondary network to make the final prediction. This strategy proves to be very robust for identifying the source camera model, even when the original image is post-processed. Our model has been trained and tested on the Forensic Camera-Model Identification Dataset provided for the IEEE Signal Processing (SP) Cup 2018. During testing we achieved an overall accuracy of 98.37%, which is the current state-of-the-art on this dataset using a single model. We used transfer learning and tested our model on the Dresden Database for Camera Model Identification, with an overall test accuracy of over 99% for 19 models. In addition, we demonstrate that the proposed pipeline is suit- able for other image-forensic classification tasks, such as, detecting the type of post-processing applied to an image with an accuracy of 96.66% - which indicates the generality of our approach.

awesome-steganalysis-project

🍿️some awesome Steganalysis

challenge-iclr-2022

GitHub repository for the ICLR Computational Geometry & Topology Challenge 2021

data-analysis-for-forensic-images

digital-image-forensics

Digital Image Forensics to indentify the source camera

forensicimages

Final project for DA

forensics-tool-hybrid-g-prnu-extractor

State of the Art Image/Video Forensics Tool : Hybrid-G-PRNU-Extractor based on the work of Muammar, to additionally extract green channel photo response noise uniformity (G-PRNU) with customization of different sizes and interpolation methods. Also additionally, Li's PRNU enhancement is implemented.

image-forensics-mfsec17

Automated Large Scale Image Forensics using Tika and Tensorflow

image-forgery-detection-cnn-vs-transfer-learning-comparison

The binary classification problem focused on first IEEE Image forensics challenge-phase 1, to predict the given image is pristine or manipulated/edited/fake. Comparing CNN & Transfer Learning models for the problem and boosting the performance by feature extraction

image_forensics

A general approach to detect tampered and generated image

imageforensics

Learning Manipulation-Invariant Image Similarity for Detecting Re-Use of Images in Scientific Publications

ml-nlp

此项目是机器学习(Machine Learning)、深度学习(Deep Learning)、NLP面试中常考到的知识点和代码实现，也是作为一个算法工程师必会的理论基础知识。

nlp-tutorial

Natural Language Processing Tutorial for Deep Learning Researchers

openmodelsappliedtoforensicimages

Application of Imagenet and NSFW models to forensic images

repnet

Laplacian Convolutional Neural Networks to forensics recaptured image

sklearn-matlab

Machine learning in Matlab using scikit-learn syntax

steganography

Computer Forensics with Image Processing and Steganography.