27 skills found
GuidoBartoli / SherloqAn open-source digital image forensic toolset
ostafen / DiglerDigler 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.
Gadzhovski / TRACE Forensic ToolkitDigital forensic analysis tool that provides a user-friendly interface for investigating disk images.
rhowardstone / Epstein Research DataStructured data exports from forensic analysis of the 218GB DOJ Epstein file release — knowledge graph, entity extractions, image catalog, EFTA mapping
karthik997 / Forensic ToolkitMajor tools used for Digital Forensic Investigation, includes tools used for Image, Audio, Memory, Network and Disk Image data analysis. Helpful resource for CTF Challenges.
sentenza / GIMP ELAA JPEG Error Level Analysis forensic plugin for the GNU Image Manipulation Program (GIMP)
quangntenemy / SteganabaraAdvanced open-source steganalysis toolkit for uncovering hidden data in images. Includes bit-layer, color, and histogram analysis modules for forensic investigation and CTF use.
Lakshit-Gupta / Image Forgery DetectionImage Forgery Detection using CNN is a deep learning–based forensic tool designed to detect splicing forgeries in digital images. The project leverages advanced feature extraction techniques—including Error Level Analysis (ELA) combined with a VGG19 backbone and focal loss—to differentiate authentic images from tampered ones.
swatijha2496 / FACE RECOGNITION USING OPENCV IN PYTHONFace is most commonly used biometric to recognize people. Face recognition has received substantial attention from researchers due to human activities found in various applications of security like airport, criminal detection, face tracking, forensic etc. Compared to other biometric traits like palm print, Iris, finger print etc., face biometrics can be non-intrusive. They can be taken even without user’s knowledge and further can be used for security based applications like criminal detection, face tracking, airport security, and forensic surveillance systems. Face recognition involves capturing face image from a video or from a surveillance camera. They are compared with the stored database. Face biometrics involves training known images, classify them with known classes and then they are stored in the database. When a test image is given to the system it is classified and compared with stored database. Face biometrics is a challenging field of research with various limitations imposed for a machine face recognition like variations in head pose, change in illumination, facial expression, aging, occlusion due to accessories etc.,. Various approaches were suggested by researchers in overcoming the limitations stated. 72 Automatic face recognition involves face detection, feature extraction and face recognition. Face recognition algorithms are broadly classified into two classes as image template based and geometric feature based. The template based methods compute correlation between face and one or more model templates to find the face identity. Principal component analysis, linear discriminate analysis, kernel methods etc. are used to construct face templates. The geometric feature based methods are used to analyze explicit local features and their geometric relations (elastic bung graph method). Multi resolution tools such as contour lets, ridge lets were found to be useful for analyzing information content of images and found its application in image processing, pattern recognition, and computer vision. Curvelets transform is used for texture classification and image de-noising. Application of Curvelets transform for feature extraction in image processing is still under research.
TheProGhost / Digital Forensics CaseStudyThe forensic analysis write-up / walkthrough for forensic disk image.
metacore-stack / Localizing Visual SplicesDetects and pinpoints image splicing by fusing forensic signal analysis with deep localization masks.
Mohammed-razin-cr / Deepfake Image Detection Deepfake Detection and Digital Forensics is a lightweight tool for identifying manipulated images using forensic analysis. It detects anomalies in metadata, pixel structures, and visual artifacts without machine learning. The system classifies images as Authentic, Suspicious, or Deepfake.
sarveshvetrivel / StegrootAll-in-one Linux CLI for CTF steganography & forensic image analysis. Automates exiftool, zsteg, binwalk, steghide, and more — one command, full report, organized output.
spider863644 / ReconEXIFAdvanced metadata and forensic analysis tool for images, PDFs, audio, and video files — with EXIF, hashes, steganography detection, and more.
NicolasPauferro / Lawliet ForensicsA powerful, extensible forensic tool designed to recover deleted files from disk images through advanced signature analysis.
hsprince-14 / Project Aletheia 2.0Image Forensics and Deepfake Detection using Fast Fourier Transform (FFT) analysis.
aancw / PolyscanPolyScan is a high-performance security scanner designed to detect and extract embedded executables, scripts, and suspicious content hidden within image files. It's specifically built for defensive security analysis and forensic investigation of image polyglots.
TanayYadav22 / OSINT TOOLS 2025OSINT TOOLS 2025 is a curated list of modern open-source intelligence tools for digital investigators, analysts, and journalists. It includes resources for username tracking, image forensics, social media scraping, geolocation, and domain analysis, tailored for ethical and lawful use in today’s digital age.
CodeRafay / Forensic Image Analysis ToolkitA Python-based desktop tool for detecting digital image forgeries. It applies forensic techniques—Error Level Analysis, Metadata Extraction, Histogram, Noise Map, JPEG Ghost, and Copy-Move Detection—to reveal inconsistencies and visual clues, helping experts assess image authenticity without automation.
Dhype7 / ForensicsMainHandModern digital forensics toolkit: image, file, and cryptography analysis (EXIF, stego, ciphers, carving, OCR). By Dhype7 (NYX team).
