ObscuraAI Specification

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ObscuraAI

Exploring, Testing, and Defending Machine Vision

ObscuraAI is an open source specification for a generative AI system designed to explore, test, analyze, and improve the resilience of machine vision systems through the generation and evaluation of adversarial visual interference patterns.

The system provides a modular research framework for generating visual patterns, defining adversarial objectives, evaluating computer vision behavior, measuring model failures, analyzing transferability, testing physical-world conditions, and developing defensive strategies.

ObscuraAI is designed for controlled and authorized research. Its purpose is to provide a structured environment for understanding machine vision weaknesses and improving model robustness rather than providing uncontrolled mechanisms for interfering with third-party systems.

Purpose

ObscuraAI establishes a reusable framework for investigating how machine vision systems respond to deliberately generated visual interference.

The specification supports a research cycle in which the system can:

Generate visual interference patterns.
Define measurable adversarial objectives.
Evaluate authorized vision models.
Measure changes in model behavior.
Optimize generated patterns.
Test patterns across multiple models.
Simulate real-world visual conditions.
Measure pattern robustness.
Analyze transferability.
Compare machine perception with human perception.
Track pattern evolution and experiment lineage.
Identify model weaknesses.
Generate defensive test cases.
Evaluate mitigation strategies.
Retest models after defensive improvements.

The system is intended to remain model-independent and modular so that individual components can be replaced, extended, or independently developed.

Design Principles

ObscuraAI should follow these principles:

Modularity
Functional capabilities should be separated into independently extensible modules.
Model Independence
The specification should not require dependence on a particular computer vision model, provider, architecture, or implementation.
Controlled Evaluation
Testing should be performed against explicitly authorized models, datasets, environments, or research targets.
Reproducibility
Experiments should retain sufficient metadata to reproduce generation and evaluation processes.
Measurability
Adversarial behavior should be represented through measurable objectives and evaluation metrics.
Defensive Research
Generated interference should support both vulnerability research and the development of defensive techniques.
Human Oversight
Researchers should be able to review experiments, objectives, generated patterns, evaluation results, and conclusions.
Transparency
Experiment results should clearly distinguish observed behavior from assumptions, predictions, and interpretations.
Extensibility
New generation methods, evaluation techniques, objectives, and defensive mechanisms should be implementable through modules or plugins.
Controlled Scope
The system should provide mechanisms for defining authorized evaluation boundaries and preventing unintended testing.

System Architecture

ObscuraAI consists of core modules that provide the primary research functionality and optional plugin modules that extend the system with specialized capabilities.

Core modules should function together as an integrated research pipeline while remaining independently replaceable where practical.

Optional plugins should be able to extend capabilities without requiring changes to the fundamental specification.

Core Modules

Generative Pattern Engine

The Generative Pattern Engine creates visual interference patterns for controlled evaluation.

The module should support multiple pattern-generation approaches, including:

Procedural patterns.
Geometric patterns.
Textural patterns.
Color-field patterns.
Frequency-based patterns.
Fractal patterns.
Stochastic patterns.
Multi-scale patterns.
Spatially adaptive patterns.
Boundary-disruptive patterns.
Shape-disruptive patterns.
Texture-disruptive patterns.
Composite patterns.
Generated image overlays.
Generated surface patterns.

The engine should support configurable generation parameters and maintain generation metadata for every resulting pattern.

Each generated pattern should receive a unique identifier and retain its generation lineage.

Adversarial Objective Engine

The Adversarial Objective Engine defines what a generated pattern is intended to influence or measure.

Supported objectives may include:

Reducing detection confidence.
Increasing classification uncertainty.
Causing classification changes.
Disrupting object localization.
Altering bounding box placement.
Disrupting segmentation.
Affecting pose estimation.
Interrupting object tracking.
Increasing model disagreement.
Producing false negatives.
Producing false positives.
Changing detected object boundaries.
Reducing recognition consistency.
Measuring sensitivity to visual interference.

The engine should support both targeted and untargeted objectives.

Objectives should be represented as measurable evaluation criteria rather than qualitative descriptions alone.

Vision Model Evaluation Engine

The Vision Model Evaluation Engine evaluates generated patterns against authorized computer vision systems.

The module should record relevant model responses, including where applicable:

Detection confidence.
Classification confidence.
Predicted classes.
Bounding boxes.
Segmentation masks.
Object boundaries.
Pose estimates.
Tracking continuity.
Model confidence changes.
Prediction changes.
False positives.
False negatives.
Model disagreement.
Processing outcomes.

The module should establish baseline measurements before interference is introduced so that changes can be evaluated against an unmodified reference condition.

Generative Optimization Engine

The Generative Optimization Engine improves generated patterns through iterative evaluation.

The optimization cycle should support a process such as:

Generate → Test → Measure → Mutate → Re-test → Rank → Refine

The engine should be capable of evaluating multiple candidate patterns and ranking them according to defined objectives.

Optimization criteria may include:

Model disruption.
Objective performance.
Pattern robustness.
Cross-model performance.
Environmental stability.
Generalization.
Pattern complexity.
Visibility cost.
Perturbation magnitude.
Reproducibility.

Optimization should preserve pattern lineage so that researchers can determine how a successful or unsuccessful pattern evolved.

Transferability Engine

The Transferability Engine evaluates whether observed effects persist across different machine vision systems or configurations.

The module should support comparison across:

Model architectures.
Model versions.
Model configurations.
Training variations.
Input resolutions.
Dataset conditions.
Camera conditions.
Environmental conditions.

Transferability results should distinguish between patterns that affect a single model and patterns that demonstrate broader cross-model behavior.

Physical World Simulation Module

The Physical World Simulation Module evaluates visual interference under simulated environmental conditions.

Supported transformations may include:

Rotation.
Translation.
Scaling.
Perspective changes.
Distance changes.
Lighting variation.
Shadow variation.
Occlusion.
Motion blur.
Camera noise.
Image compression.
Resolution changes.
Viewing angle changes.
Environmental variation.

The module should allow multiple transformations to be combined into controlled test conditions.

Pattern Robustness Module

The Pattern Robustness Module measures how consistently a generated pattern performs across changing conditions.

A robustness profile should be capable of recording performance across:

Different viewpoints.
Different distances.
Different lighting conditions.
Different resolutions.
Different camera conditions.
Different environmental conditions.
Different model configurations.
Different input transformations.

The resulting profile should identify conditions where performance remains stable and conditions where the pattern loses effectiveness.

Human Perception Module

The Human Perception Module evaluates the relationship between human visual recognition and machine visual recognition.

The module may record:

Human recognition results.
Human classification confidence.
Human pattern visibility.
Human detection difficulty.
Machine recognition results.
Machine classification confidence.
Differences between human and machine recognition.

The purpose is to investigate situations in which visual information remains recognizable to humans while producing substantially different responses from machine vision systems.

Adversarial Pattern Catalog

The Adversarial Pattern Catalog maintains structured records for generated patterns and their evaluation history.

Pattern records should support information including:

Pattern identifier.
Pattern description.
Generation configuration.
Generation method.
Adversarial objective.
Target model category.
Evaluation models.
Input conditions.
Environmental conditions.
Perturbation characteristics.
Evaluation results.
Robustness measurements.
Transferability measurements.
Human perception measurements.
Pattern lineage.
Experiment identifiers.
Version information.
Reproducibility metadata.

The catalog should support searching, filtering, comparison, and historical analysis.

Pattern Lineage Module

The Pattern Lineage Module records how patterns evolve through the generation and optimization process.

Lineage records should identify:

Parent patterns.
Child patterns.
Generation methods.
Mutation operations.
Optimization stages.
Evaluation results.
Selection decisions.
Objective changes.
Environmental testing conditions.

Researchers should be able to trace a final pattern back through its generation history.

Defensive Research Module

The Defensive Research Module uses generated visual interference as controlled test cases for improving machine vision resilience.

The module should support:

Vulnerability identification.
Failure-condition analysis.
Defensive test generation.
Robustness testing.
Mitigation evaluation.
Before-and-after comparisons.
Regression testing.
Defensive benchmarking.
Retesting after model changes.

Defensive results should distinguish between a model’s original performance, observed weaknesses, applied mitigation, and post-mitigation performance.

Experiment Management Module

The Experiment Management Module manages controlled research sessions.

Each experiment should define:

Research objective.
Authorized evaluation targets.
Input conditions.
Baseline conditions.
Generated patterns.
Evaluation models.
Evaluation metrics.
Environmental conditions.
Experiment parameters.
Results.
Researcher annotations.
Conclusions.

Experiments should maintain immutable historical records where practical so that results can be audited and reproduced.

Evaluation Metrics Module

The Evaluation Metrics Module provides standardized measurements for comparing experiments and generated patterns.

Metrics may include:

Detection change.
Classification change.
Confidence change.
Localization change.
Segmentation change.
Tracking disruption.
Model disagreement.
Robustness.
Transferability.
Environmental sensitivity.
Perturbation magnitude.
Pattern visibility.
Reproducibility.

The specification should permit additional metrics to be introduced without changing existing experiment records.

Authorization and Evaluation Boundary Module

The Authorization and Evaluation Boundary Module establishes the permitted scope of experimentation.

The module should support:

Explicit target registration.
Authorized model registration.
Authorized dataset registration.
Experiment scope definitions.
Research environment definitions.
Evaluation permissions.
Rate limits.
Local or isolated testing boundaries.
Experiment logging.
Human approval requirements.

The system should prevent evaluation workflows from silently expanding beyond their declared research scope.

Research Audit Module

The Research Audit Module maintains records of significant system activity.

Audit records should support:

Experiment creation.
Pattern generation.
Pattern modification.
Model registration.
Evaluation execution.
Objective changes.
Configuration changes.
Result generation.
Defensive testing.
Researcher annotations.
Export activity.

Audit records should support investigation of how conclusions and results were produced.

Optional Plugin Modules

Optional plugins may extend ObscuraAI with specialized research capabilities.

Multi-Model Adversarial Generator Plugin

Generates patterns optimized against multiple authorized models simultaneously.

The plugin may compare model-specific objectives and identify patterns that produce consistent effects across different model families.

Black-Box Evaluation Plugin

Supports evaluation where internal model parameters and architecture are unavailable.

The plugin should rely on observable model responses and controlled queries while maintaining evaluation boundaries and rate limits.

White-Box Research Plugin

Provides advanced research capabilities when authorized access to model internals is available.

The plugin may support analysis of internal model behavior, gradients, feature representations, or other research signals.

Universal Pattern Plugin

Investigates patterns intended to demonstrate effects across broad classes of authorized vision models rather than a single model.

Scene-Aware Pattern Generator Plugin

Generates interference patterns based on scene context, object characteristics, environmental conditions, or spatial relationships.

Temporal Interference Plugin

Extends visual interference research into sequences of changing visual inputs.

The plugin may evaluate temporal consistency, frame-to-frame recognition, and tracking behavior.

Adversarial Video Plugin

Extends the platform from individual images to controlled video evaluation.

The plugin should support frame-level and sequence-level measurements.

Pattern Explainability Plugin

Analyzes generated patterns to identify characteristics associated with observed model responses.

The plugin may provide visual, statistical, or feature-level explanations of pattern behavior.

Model Fingerprinting Plugin

Analyzes observable model responses to determine whether different systems demonstrate distinguishable behavioral characteristics.

Robustness Benchmark Plugin

Provides standardized benchmark scenarios for comparing machine vision robustness across experiments, models, and versions.

AI Research Agent Plugin

Provides an AI-assisted research workflow capable of proposing experiments, generating hypotheses, analyzing results, identifying patterns in experimental data, and suggesting defensive research directions.

All recommendations produced by the research agent should remain subject to human review.

Synthetic Environment Plugin

Provides controlled synthetic environments for evaluating visual interference under repeatable conditions.

Pattern Comparison Plugin

Provides side-by-side and statistical comparison of generated patterns based on defined objectives, robustness, transferability, visibility, and environmental performance.

Dataset Evaluation Plugin

Evaluates patterns across authorized research datasets and records dataset-specific behavior.

Pattern Generation Lifecycle

ObscuraAI should support a repeatable pattern research lifecycle:

Define

The researcher defines the objective, authorized evaluation scope, target models, baseline conditions, and evaluation metrics.

Generate

The Generative Pattern Engine produces one or more candidate patterns.

Baseline

The Vision Model Evaluation Engine establishes baseline model behavior without visual interference.

Evaluate

Generated patterns are evaluated against the authorized model or research environment.

Measure

The Evaluation Metrics Module records changes in model behavior.

Optimize

The Generative Optimization Engine modifies and ranks candidate patterns according to the defined objective.

Generalize

The Transferability Engine and Physical World Simulation Module evaluate whether observed behavior persists across models and conditions.

Analyze

The researcher examines results, pattern lineage, environmental sensitivity, and model behavior.

Defend

The Defensive Research Module uses the observed weaknesses to develop and evaluate mitigation strategies.

Retest

The system evaluates the modified or defended model against previous and newly generated test patterns.

Pattern Scoring

ObscuraAI should provide a standardized scoring framework for comparing generated patterns.

A composite score may incorporate:

Objective effectiveness.
Robustness.
Transferability.
Environmental stability.
Generalization.
Perturbation magnitude.
Pattern complexity.
Human visibility.
Reproducibility.

Individual component scores should remain available so that researchers can understand why a pattern received a particular overall ranking.

Composite scoring should never replace the underlying measurements.

Experiment Reproducibility

Experiments should preserve sufficient information to reproduce results.

Reproducibility records should include:

Pattern identifiers.
Generation parameters.
Objective definitions.
Evaluation conditions.
Model identifiers.
Dataset identifiers.
Environmental parameters.
Transformation parameters.
Metric definitions.
Experiment timestamps.
Software-independent configuration information where applicable.
Result records.
Pattern lineage.

Changes to experimental parameters should create a new experiment or version rather than silently modifying historical results.

Model Independence

ObscuraAI should not assume a particular machine vision architecture.

The specification should support evaluation of:

Object detection systems.
Image classification systems.
Image segmentation systems.
Pose estimation systems.
Object tracking systems.
Multimodal vision systems.
Other machine perception systems capable of accepting visual inputs.

Model adapters may translate model-specific outputs into standardized ObscuraAI evaluation records.

Interoperability

Modules should communicate through clearly defined data structures and interfaces.

The specification should support interoperability between:

Pattern generators.
Evaluation engines.
Optimization engines.
Model adapters.
Experiment managers.
Metric systems.
Defensive research systems.
Pattern catalogs.
Research agents.
Visualization and analysis tools.

A module should be replaceable without requiring the redesign of unrelated modules.

Data Integrity

ObscuraAI should preserve the integrity of experimental records.

The system should:

Preserve original experiment results.
Track modifications.
Record pattern lineage.
Identify model versions.
Record evaluation conditions.
Distinguish generated data from observed data.
Distinguish measurements from researcher interpretations.
Maintain audit information.
Prevent accidental overwriting of historical experiments.

Human Oversight

Human researchers should remain responsible for defining research objectives, authorizing evaluation targets, reviewing results, interpreting findings, and approving defensive actions.

AI-assisted functions may propose patterns, experiments, hypotheses, or interpretations, but those functions should not independently expand authorized evaluation scope.

Security and Responsible Research

ObscuraAI is intended for authorized machine vision research, security testing, robustness evaluation, and defensive development.

Implementations should provide safeguards appropriate to the deployment environment, including:

Explicit evaluation authorization.
Controlled target registration.
Isolated research environments where appropriate.
Rate limiting.
Experiment logging.
Human approval for sensitive operations.
Clear separation between research targets and unauthorized systems.
Reproducible experiment records.
Access controls.
Research scope enforcement.

The system should be designed so that experimental capabilities are directed toward controlled research environments rather than unrestricted third-party targeting.

Defensive Applications

ObscuraAI should support defensive applications including:

Machine vision robustness testing.
Adversarial example research.
Model vulnerability discovery.
Computer vision quality assurance.
Safety testing.
Model regression testing.
Dataset robustness research.
Defensive benchmark creation.
Mitigation validation.
Research into machine perception limitations.

Generated patterns should be reusable as defensive test cases after their initial discovery.

Research Outputs

ObscuraAI should support generation of research outputs including:

Pattern records.
Experiment reports.
Robustness profiles.
Transferability reports.
Model comparison reports.
Failure-condition summaries.
Defensive evaluation reports.
Pattern lineage records.
Benchmark results.
Research datasets.
Reproducibility records.

Specification Branding License (SBL)

Standard

Fully AGPL-3.0+ compliant system
Copyleft enforced for network deployments
Required attribution:
- Roxanne Ardary
- https://www.roxanneardary.com/

Optional

Specification Branding License (SBL)
- Attribution-free commercial deployment
- Pricing based on scale, usage, and deployment scope
- https://roxanneardary.com/obscuraai/

License & Notice Requirements

ObscuraAI is released under the GNU Affero General Public License v3.0 or later (AGPL-3.0+).
By contributing to any Open Arsenal project, you agree that your contributions will also be released under this license.

Please note the following:

All contributions must comply with the AGPL-3.0+ terms.
Under Section 7 of the license, all redistributions, forks, and derivative works must preserve attribution to:
Roxanne Ardary and roxanneardary.com.
ObscuraAI specifications are free to use with attribution. A Specification Branding License can be negotiated upon request.
The project’s notice.md file tracks attribution requirements and contributor acknowledgments.
Any update that adds new contributors or modifies attribution should also update notice.md.
When submitting a pull request, ensure that any new files maintain the attribution headers where applicable.
Network-deployed versions of this software must also remain fully AGPL-3.0+ compliant, including exposure of source code modifications when applicable under the license.

For full legal details, please refer to the AGPL-3.0+ license and the project’s notice.md file.

Notice – ObscuraAI

Attribution Requirement: Under Section 7 of the AGPL 3.0+ license, all redistributions, forks, and derivative works, including network-deployed versions of this project, must provide attribution to Roxanne Ardary and roxanneardary.com.

Contributors

This file tracks contributors and their specific contributions to the project.

Roxanne Ardary, roxanneardary.com – September 4, 2026
Created the repository for ObscuraAI. Created the specification for a generative AI system focused on exploring, testing, and defending machine vision through the generation and evaluation of adversarial visual interference patterns.
[Add other contributors here] – [Date]
[Describe contribution in one sentence]

License – ObscuraAI

This repository is licensed under the GNU Affero General Public License v3.0 or later (AGPL-3.0+).

Key Points

You are free to use, modify, and distribute the code.
All redistributions, forks, and derivative works or network-deployed versions must also be licensed under AGPL-3.0+ and provide attribution to Roxanne Ardary and roxanneardary.com as required under Section 7 of the license.
The software is provided “as is,” without warranty of any kind.

For the full license text, see GNU AGPL-3.0 License.