file-carving

Digler is a tool for forensic disk analysis and file recovery. It's designed to help you unearth lost or deleted data from various disk images and raw devices.

Digital forensic analysis tool that provides a user-friendly interface for investigating disk images.

Extract SysCfg data from corrupted or unreadable NAND dumps.

Browser-based CTF toolkit. Drop a file, every check runs at once, nothing uploads. Hand-written Rust/WASM core, zero dependencies.

A framework to generate file carving test data.

This project was done as part of the Digital Forensics Analysis coursework, specifically the file carving tool.

JPEG Restorer - A file carving technique developed in C exploiting the syntactic structure of the JPEG file format while using thumbnail affinity for semantic analysis.

File recovery tool for the FAT file system

PNG file carving related material

Free, read-only photo & video recovery for a formatted SD card — JPEG/RAW/video, one Python file, macOS & Linux

A file carving tool that scoops the sweet evidence from the hive of raw data

A tool to extract INES-formated NES roms from any file. This tool was originally designed for the Wii Virtual Console 00000001.app file.

A WIP high performance, precise file carving tool developed for my honours project at uni

You deleted your only copy of an OpenPGP private key file. You wonder if the key material can be recovered via forensic techniques such as file carving. Dodge may be able to help!

一个 NTFS 文件恢复工具，支持 MFT 扫描、签名搜索、智能覆盖检测和高性能文件恢复

Data forensics for RPG Maker 2000 projects

File carving tool for forensic image recovery

A configuration enabling the Scalpel data recovery / data carving tool to work with deleted private key data!

Interactive forensic file recovery tool for USB drives and SD cards using Foremost. Recovers 100% of files from formatted/deleted partitions.

Deep Learning for file type Identification in Digital Forensics. We use the first, body, and last blocks of bytes on the disk to account for all possible scenarios and train the FFNN, CNN, GRU, and LSTM models. Afterward, we make predictions and evaluate the performance of each model.