image forensics and metadata extraction tool

This project is a steganography analysis toolkit and digital forensics suite designed to detect, extract, and embed hidden data within image and audio files. It provides a dockerized security environment that bundles various analysis tools into a containerized workspace, including a media spectrogram visualizer for revealing visually hidden patterns. The toolkit features a dedicated brute force system for recovering password-protected messages using automated wordlists and candidate password testing. It distinguishes itself by providing rule-based wordlist generation that uses expansion patte

ExifTool is a command line application and batch metadata processor used for reading, writing, and editing EXIF, IPTC, XMP, and other metadata in image and video files. It functions as a file metadata editor and image metadata extractor capable of modifying embedded internal properties across a wide variety of supported file formats. The tool specializes in extracting camera maker notes and industry standard descriptors from digital media. It supports metadata migration workflows, allowing embedded information to be copied from a source file and applied to a destination file. The utility cov

Imago is a python tool that extract digital evidences from images recursively. This tool is useful throughout a digital forensic investigation. If you need to extract digital evidences and you have a lot of images, through this tool you will be able to compare them easily. Imago allows to…

Upscayl is a cross-platform desktop application designed to increase the resolution and visual quality of digital images using artificial intelligence. By executing all processing tasks locally on the user's machine, the software ensures that sensitive media files remain private and never leave the host system for cloud-based services. The application distinguishes itself through a hardware-agnostic architecture that offloads intensive rendering workloads directly to the local graphics unit. It utilizes a hardware abstraction layer to translate enhancement commands into instructions compatibl

This project is a command-line tool designed for image super-resolution and noise reduction, with a primary focus on anime-style illustrations. It utilizes convolutional neural network inference to reconstruct missing pixel data and remove digital artifacts, allowing users to upscale images and reduce noise either independently or in a single simultaneous processing pass. Beyond its core image restoration capabilities, the software provides a comprehensive suite for machine learning model training. Users can prepare custom datasets and optimize neural networks for specific restoration tasks,

exif-js is a JavaScript library for extracting image metadata directly in the browser. It functions as a binary image buffer parser that reads raw image bytes to retrieve technical details without requiring files to be uploaded to a server. The library parses data following the EXIF and IPTC standards to extract camera settings, timestamps, GPS coordinates, captions, keywords, and copyright information. It utilizes typed arrays and offset-based traversal to navigate image structures and map numeric identifiers to human-readable tags.

android-gpuimage is an OpenGL image processing library for Android that provides a GPU-powered framework for applying real-time shaders, blending layers, and processing live video streams. It functions as a real-time video filter and image blending engine to modify the luminance, vibrance, and geometry of visual content. The library enables the application of graphical effects to both static photos and live camera feeds. It supports complex image transformations, including the merging of multiple image layers through various blending modes. Capabilities cover a broad range of image manipulat

Caire is a command-line image processing engine designed for content-aware resizing and batch manipulation. It utilizes seam carving algorithms to adjust image dimensions by identifying and removing low-energy pixels, allowing for the rescaling of images while preserving primary visual subjects and maintaining aspect ratios. The tool distinguishes itself through its ability to protect specific visual elements, such as human faces, from distortion during the resizing process. Users can apply custom binary masks to define regions for protection or forced removal, and the engine provides real-ti

opencv4nodejs is a set of JavaScript wrappers and a C++ native addon that provides Node.js bindings for the OpenCV library. It functions as a computer vision library and image processing framework, exposing high-performance C++ algorithms to a JavaScript environment. The project enables the execution of vision algorithms for detecting faces, tracking objects, and analyzing visual data using deep neural networks. It includes capabilities for data pattern classification, text pattern recognition, and the identification of facial landmarks and gestures. The framework covers a broad capability s

This project is a collection of optional, community-contributed algorithms and specialized vision tools that extend the core OpenCV framework. It serves as a comprehensive library of extra modules for computer vision research, providing advanced toolsets for image processing, visual data analysis, and object detection. The library includes specialized frameworks for augmented reality tracking, biometric face recognition, and three-dimensional pose estimation. It provides distinct capabilities for identifying AR markers, tracking 3D object silhouettes, and performing neural network vulnerabili

Libvips is a C-based image processing library designed to manipulate large visual assets through a low-memory, parallel processing pipeline. It functions as a streaming image processor that avoids loading entire files into system memory, enabling the handling of massive images in resource-constrained environments. The library distinguishes itself through a demand-driven architecture that constructs a deferred execution plan, computing only the necessary pixels for a final output. By utilizing a cache-friendly tiled processing model and memory-mapped file access, it minimizes latency and redun