SceneMatch

v0.0.1 suspicious
6.0
Medium Risk

Matching tools for SceneAPI

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows minimal risks in terms of network, shell, obfuscation, and credential handling. However, its metadata risk is high due to recent creation, low activity, and a single contributor, raising concerns about potential supply-chain attacks.

  • High metadata risk
  • Recently created repository
  • Low activity
  • Single contributor
Per-check LLM notes
  • Network: No network calls detected, which is normal unless the package's functionality requires external communication.
  • Shell: No shell execution patterns detected, indicating no direct system command execution from the package.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: High risk due to recent repository creation, low activity, single contributor, and new maintainer.

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

✓ Code Obfuscation

No obfuscation patterns detected

✓ Shell / Subprocess Execution

No shell execution patterns detected

✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

No author email provided

✓ Suspicious Page Links

All external links appear legitimate

⚠ Git Repository History score 10.0

Git history flags: Repository created very recently: 5 day(s) ago (2026-06-01T05:07:38Z)

  • Repository created very recently: 5 day(s) ago (2026-06-01T05:07:38Z)
  • Repository has zero stars and zero forks
  • Very few commits: 1 total
  • Single contributor with only 1 commit(s) — possibly throwaway account
⚠ Maintainer History score 4.0

2 maintainer concern(s) found

  • Only one version has ever been released — brand new package
  • Author "SceneAPI" appears to have only 1 package on PyPI (new or inactive account)
✓ Known CVE Vulnerabilities

No known vulnerabilities found in OSV database.

💡 AI App Starter Prompt

Use this prompt to build a project with SceneMatch 
Develop a comprehensive scene matching application using the Python package 'SceneMatch'. This application will serve as a tool for professionals and enthusiasts in fields such as computer vision, robotics, and augmented reality to match scenes based on their visual characteristics. The app will leverage the SceneAPI and SceneMatch's advanced algorithms to identify and compare scenes from various sources, including images and videos.

### Project Overview:
- **Name**: SceneMatcher Pro
- **Goal**: To create a user-friendly application that allows users to upload scenes (images/videos) and find matches based on visual similarity.
- **Target Audience**: Developers, researchers, and hobbyists interested in computer vision and scene recognition.

### Core Features:
1. **Scene Upload & Storage**
   - Users should be able to upload scenes (images/videos).
2. **Scene Matching**
   - Utilize SceneMatch's tools to compare uploaded scenes against a database of pre-existing scenes.
3. **Results Presentation**
   - Display matching scenes with confidence scores indicating the degree of similarity.
4. **Advanced Filtering Options**
   - Allow users to filter results based on specific criteria such as time of day, weather conditions, etc.
5. **User Profiles & Saved Scenes**
   - Enable users to create profiles and save scenes for future reference.
6. **Integration with External Datasets**
   - Provide an option to import scenes from external datasets supported by SceneAPI.

### Implementation Steps:
1. **Setup Environment**
   - Install necessary Python packages, including SceneMatch, SceneAPI, and others required for image/video processing.
2. **Database Design**
   - Design a database schema to store scene metadata and features extracted using SceneMatch.
3. **Frontend Development**
   - Develop a simple yet intuitive frontend interface using web technologies like HTML, CSS, and JavaScript.
4. **Backend Development**
   - Implement backend logic for handling scene uploads, storage, and matching using Flask or Django.
5. **Scene Matching Algorithm**
   - Integrate SceneMatch into the backend to perform scene comparisons and return matches.
6. **Testing & Optimization**
   - Test the application thoroughly to ensure accurate scene matching and optimal performance.
7. **Deployment**
   - Deploy the application on a cloud platform like AWS or Heroku.

### Utilizing SceneMatch Package:
- Use SceneMatch to preprocess and extract features from uploaded scenes.
- Leverage SceneMatch’s comparison tools to find matches between uploaded scenes and those stored in the database.
- Display the results in a user-friendly manner, highlighting key differences and similarities.

By completing this project, you'll gain valuable experience working with advanced computer vision tools and developing practical applications that solve real-world problems.