Results for “forensic-imaging”

Conducts digital forensics investigations on compromised endpoints, including memory acquisition, disk imaging, artifact analysis, and timeline reconstruction for incident response and evidence collection.

Create forensically sound bit-for-bit disk images using dd and dcfldd while preserving evidence integrity through hash verification.

Perform comprehensive forensic analysis of disk images using Autopsy to recover files, examine artifacts, and build investigation timelines.

Investigate compromised Docker containers by analyzing images, layers, volumes, logs, and runtime artifacts to identify malicious activity and evidence.

Guides authorized digital forensics and incident response workflows including memory dump analysis, disk timeline creation, PCAP investigation, and artifact triage with evidence preservation.

Collect volatile forensic evidence from a compromised system following order of volatility, preserving memory, network connections, processes, and system state before they are lost.

Flamingo: A Visual Language Model for Few-Shot Learning

Master memory forensics techniques including memory acquisition, process analysis, and artifact extraction using Volatility and related tools. Use when analyzing memory dumps, investigating incidents, or performing malware analysis from RAM captures.

Import object detection models from HuggingFace or NVIDIA NGC into a DeepStream pipeline with automated ONNX download, TensorRT engine build, custom parser, multi-stream benchmark, and PDF report generation.

Trains and fine-tunes vision models for object detection, image classification, and segmentation using Hugging Face Transformers on cloud GPUs, with automatic dataset validation and Hub persistence.

Trains, evaluates, exports, and runs inference for person re-identification models using TAO, learning discriminative embeddings for cross-camera matching.

Trains, evaluates, exports, quantizes, and runs inference for Sparse4D multi-camera temporal 3D object detection and tracking models using TAO.

Build forensic super timelines from disk images using Plaso (log2timeline) and triage them in Timesketch.

Acquires Linux memory using the LiME kernel module and analyzes the image with Volatility 3 to extract processes, network connections, bash history, kernel modules, and injected code for incident response.

Recover files from disk images and unallocated space using Foremost's header-footer signature carving to extract evidence regardless of file system state.

This skill recommends performing an imaging study to assess tumor size, appearance, and parasellar extent once biochemical diagnosis of acromegaly is confirmed. Trigger when IGF-1 is elevated and GH fails to suppress to <0.4 µg/L during an oral glucose tolerance test.

Perform forensic on-chain analysis of EVM tokens to detect scams, rug pulls, and soft rugs by cross-referencing on-chain state against team narratives.

Parse Windows LNK shortcut files to extract target paths, timestamps, volume information, and machine identifiers for forensic timeline reconstruction.

Collect, parse, and correlate system, application, and security logs to reconstruct events and establish timelines during forensic investigations.

Parse Windows Prefetch files to determine program execution history including run counts, timestamps, and referenced files for forensic investigation.

Analyzes RAM memory dumps from compromised systems using the Volatility framework to identify malicious processes, injected code, network connections, loaded modules, and extracted credentials.

Extract and analyze browser history, cookies, cache, downloads, and bookmarks from Chrome, Firefox, and Edge for forensic evidence of user web activity.

Analyze RAM dumps with Volatility 3 to detect malware, process injection, network connections, and credential theft during incident response.

Build comprehensive forensic super-timelines using Plaso (log2timeline) to correlate events across file systems, logs, and artifacts into a unified chronological view.

Hunt for threats in Windows Event Logs using Chainsaw, a fast Rust-based forensic tool that runs Sigma rules, keyword searches, and artifact analysis offline.

Extract and analyze Chromium-based browser artifacts using Hindsight to reconstruct user web activity for forensic investigations.

Detect poisoned training data and backdoored models across the ML pipeline using statistical analysis, activation clustering, and spectral signatures.

Multimodal Few-Shot Learning with Frozen Language Models

Analyzes firmware images for embedded malware, backdoors, and unauthorized modifications targeting routers, IoT devices, UEFI/BIOS, and embedded systems. Covers firmware extraction, filesystem analysis, binary reverse engineering, and bootkit detection.

Extract and analyze Windows Registry hives to uncover user activity, installed software, autostart entries, and evidence of system compromise.

Measures distributional similarity between original GAN-generated images and their semantically manipulated counterparts using the Fréchet Inception Distance (FID) metric.

Bayesian modeling with PyMC. Build hierarchical models, MCMC (NUTS), variational inference, LOO/WAIC comparison, posterior checks, for probabilistic programming and inference.

Analyze Windows memory dumps for signs of compromise using the Rekall memory forensics framework, including process injection, hidden processes, and rootkit detection.

Accelerate LLM inference using speculative decoding, Medusa multiple heads, and lookahead decoding techniques for 1.5-3.6× speedup without quality loss.

Read BEFORE troubleshooting or enhancing camera image quality. Diagnoses and enhances image quality from cameras connected via Image Acquisition Toolbox or USB Webcams support package. Discovers camera capabilities at runtime, analyzes captured images for quality issues (brightness, contrast, sharpness, noise, color balance, backlighting), suggests hardware setting adjustments tailored to the specific camera, and applies Image Processing Toolbox enhancement functions. Use when a user wants to improve camera image quality, troubleshoot dark/blurry/noisy/grainy/ overexposed/washed out/color cast images, or optimize camera settings.