{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"tool_deceptioner","slug":"deceptioner","name":"DecEptioner","type":"webapp","url":"https://deceptioner.site","page_url":"https://unfragile.ai/deceptioner","categories":["text-writing"],"tags":[],"pricing":{"model":"paid","free":false,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"tool_deceptioner__cap_0","uri":"capability://text.generation.language.ai.generated.text.obfuscation.with.detection.evasion","name":"ai-generated text obfuscation with detection evasion","description":"Applies algorithmic transformations to AI-generated text to reduce detectability by commercial AI detection systems (likely Turnitin, GPTZero, Originality.ai). The mechanism appears to involve lexical substitution, syntactic restructuring, and stylistic variation patterns that preserve semantic meaning while altering statistical fingerprints that detection models rely on. Implementation likely uses pattern matching against known detection heuristics (n-gram distributions, perplexity signatures, entropy markers) and applies targeted modifications to degrade classifier confidence scores.","intents":["I need to submit AI-drafted content to platforms with AI detection policies without triggering flags","I want to repurpose AI-generated marketing copy while maintaining authenticity signals to detection systems","I need to transform bulk AI content for publication channels that penalize detected AI authorship","I want to understand which parts of my AI text are most detectable and selectively modify them"],"best_for":["Content creators and marketers operating in compliance-gray zones where AI detection avoidance is operationally necessary","Academic and professional writers using AI assistance who face institutional AI detection policies","SEO and content marketing teams managing large volumes of AI-generated material for publication"],"limitations":["No published effectiveness metrics against current detection models — claims of 'precision' are unvalidated against GPTZero, Turnitin, or Originality.ai benchmarks","Detection evasion is an adversarial arms race; transformations effective today may fail against updated detection models within weeks or months","Transformation quality and semantic preservation are undocumented — risk of producing garbled or incoherent output on complex source material","No granular control visible over transformation intensity, style preservation, or domain-specific adaptation","Likely violates Terms of Service on major publishing platforms (Medium, Substack, academic submission systems) that explicitly prohibit detection evasion"],"requires":["Web browser with JavaScript enabled","Plain text or copy-paste input capability","Active internet connection to cloud transformation service","Paid subscription (freemium tier not documented)"],"input_types":["plain text","copy-pasted content from any source"],"output_types":["transformed plain text","likely downloadable as .txt or clipboard-ready format"],"categories":["text-generation-language","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_deceptioner__cap_1","uri":"capability://text.generation.language.batch.text.transformation.with.preservation.of.semantic.intent","name":"batch text transformation with preservation of semantic intent","description":"Processes multiple text passages or documents sequentially through the obfuscation pipeline, applying consistent transformation rules across a corpus while attempting to preserve domain-specific terminology, tone, and factual accuracy. The system likely maintains a transformation context or style profile to ensure coherence across batch operations, preventing inconsistent rewrites that would signal synthetic modification to human readers or statistical analysis tools.","intents":["I need to transform 50+ pages of AI-generated content at once without manual per-passage intervention","I want consistent stylistic transformation across a multi-chapter document or content series","I need to bulk-process AI content while maintaining brand voice and terminology consistency","I want to transform content in batches and compare before/after detection scores"],"best_for":["Content agencies and marketing teams managing large-scale AI content production","Publishers and authors working with AI-assisted writing at volume","SEO teams optimizing bulk-generated content for publication"],"limitations":["Batch processing speed and throughput are undocumented — unclear if processing is sequential or parallel, and latency per document is unknown","No visible progress tracking, error handling, or partial-failure recovery for large batches","Transformation consistency across batch items is unvalidated — risk of stylistic drift or incoherence across documents","No rollback or version control — transformed content cannot be reverted if detection evasion fails","Batch size limits are undocumented — unclear if there are caps on document count or total token volume"],"requires":["Web browser with file upload capability (if supported)","Plain text or document format input (specific formats unknown)","Paid subscription tier (batch processing may require higher tier)","Active internet connection"],"input_types":["plain text","multiple documents (format unclear — likely .txt, possibly .docx or .pdf)"],"output_types":["transformed plain text or documents","batch download or export format (unknown)"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_deceptioner__cap_2","uri":"capability://safety.moderation.detection.model.targeting.and.evasion.strategy.selection","name":"detection model targeting and evasion strategy selection","description":"Allows users to specify which AI detection systems they are trying to evade (e.g., GPTZero, Turnitin, Originality.ai, Copyleaks), and applies targeted transformation strategies optimized against each detector's known weaknesses or heuristics. Implementation likely maintains a database of detection model signatures, known false-positive triggers, and adversarial examples, then selects transformation rules that maximize evasion probability for the specified target detector.","intents":["I need to evade a specific detection system that my content will be checked against","I want to understand which detectors are most vulnerable to transformation and choose accordingly","I need different transformation strategies for different publication channels with different detection tools","I want to test my content against multiple detectors and optimize transformation for the hardest one"],"best_for":["Content creators targeting specific platforms with known detection policies (e.g., academic institutions using Turnitin, publishers using Originality.ai)","Marketing teams optimizing for specific client requirements or platform compliance","Researchers studying detection evasion and adversarial robustness of AI detection systems"],"limitations":["Detection model targeting assumes static detector behavior — detectors are continuously updated, making target-specific strategies obsolete quickly","No evidence that multi-detector optimization is possible without degrading evasion effectiveness against any single detector","Unclear which detectors are actually supported or how frequently target profiles are updated","No transparency on false-negative rates (content that passes detection but is flagged by human review) vs. false-positive rates","Adversarial targeting may trigger meta-detection (detectors learning to identify evasion attempts themselves)"],"requires":["Knowledge of which detection system will be used to check the content","Paid subscription (likely requires higher tier for multi-detector support)","Web interface or API access to specify target detector"],"input_types":["plain text","detector selection parameter (dropdown or API field)"],"output_types":["transformed text optimized for specified detector","likely metadata indicating target detector and confidence score"],"categories":["safety-moderation","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_deceptioner__cap_3","uri":"capability://text.generation.language.tone.and.style.preservation.during.transformation","name":"tone and style preservation during transformation","description":"Maintains stylistic attributes (formality level, vocabulary complexity, sentence structure patterns, domain-specific terminology, brand voice) while applying detection-evasion transformations. Implementation likely uses style embeddings or linguistic feature extraction to identify and preserve domain markers, then applies transformations only to statistical signatures that detection models rely on (n-gram distributions, perplexity, entropy) while leaving style-critical elements intact.","intents":["I need to transform AI content while keeping my brand voice and tone consistent","I want to preserve technical terminology and domain-specific language while evading detection","I need to maintain formality level and audience-appropriate language during transformation","I want to ensure transformed content reads naturally and doesn't signal synthetic modification to human readers"],"best_for":["Professional writers and marketers who need to maintain brand consistency across transformed content","Technical writers and domain experts using AI assistance while preserving specialized terminology","Publishers and content agencies where stylistic consistency is critical to audience trust"],"limitations":["Style preservation mechanisms are undocumented — unclear how tone/style are extracted, measured, or enforced during transformation","No user control over style preservation intensity — cannot trade off style fidelity for stronger detection evasion","Risk of style-preservation constraints conflicting with detection-evasion goals, resulting in suboptimal evasion or degraded style","No validation that preserved style actually matches source material or user intent","Domain-specific terminology preservation is likely limited to common fields — specialized jargon may be incorrectly transformed"],"requires":["Plain text input with sufficient length to extract reliable style signatures (likely 500+ words)","Paid subscription","Web interface or API access"],"input_types":["plain text","optional style reference document or tone descriptor"],"output_types":["transformed text with preserved stylistic attributes","likely metadata indicating style preservation confidence"],"categories":["text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_deceptioner__cap_4","uri":"capability://safety.moderation.real.time.detection.scoring.and.feedback","name":"real-time detection scoring and feedback","description":"Provides users with estimated detection scores or confidence metrics indicating how likely the transformed text is to be flagged by target detection systems. Implementation likely integrates with or mimics detection model APIs (GPTZero, Originality.ai) to provide real-time feedback, or uses proxy metrics (perplexity, entropy, n-gram novelty) as detection risk indicators. Users can iteratively refine transformations based on feedback to optimize evasion probability.","intents":["I want to see how detectable my transformed content is before submitting it","I need to understand which parts of my content are most at-risk of detection","I want to iteratively improve transformation until detection risk is below a threshold","I need confidence metrics to decide whether transformed content is safe to publish"],"best_for":["Content creators who want to validate transformation effectiveness before publication","Teams managing high-stakes content (academic submissions, professional publications) where detection failure has significant consequences","Users optimizing transformation strategies and learning which techniques are most effective"],"limitations":["Detection scoring is likely a proxy or estimate, not actual detection model output — real detection systems may produce different results","No transparency on how scoring is calculated or which detection models are being simulated","Scoring may be outdated relative to current detector versions — detectors are continuously updated, making historical scores unreliable","False confidence: high scores may not guarantee evasion, and low scores may not indicate certain detection","No explanation of which specific features or passages are driving detection risk — feedback is likely aggregate rather than granular","Scoring may incentivize over-transformation, producing unnatural or incoherent output"],"requires":["Transformed text output from the obfuscation capability","Paid subscription (likely requires higher tier for real-time scoring)","Web interface or API access","Active internet connection"],"input_types":["transformed plain text"],"output_types":["detection risk score (likely 0-100 or percentage)","confidence metric or probability estimate","optional granular feedback per passage or sentence"],"categories":["safety-moderation","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_deceptioner__cap_5","uri":"capability://text.generation.language.iterative.refinement.and.multi.pass.transformation","name":"iterative refinement and multi-pass transformation","description":"Allows users to apply multiple transformation passes to the same content, with each pass further modifying the text to reduce detection risk or improve specific attributes. Implementation likely maintains transformation history and allows selective application of different transformation strategies in sequence, with detection scoring feedback between passes to guide optimization. Users can experiment with different transformation intensities and combinations to find optimal balance between evasion and quality.","intents":["I want to apply multiple transformation passes to gradually reduce detection risk","I need to experiment with different transformation strategies and compare results","I want to refine transformation based on detection feedback until content meets my threshold","I need to balance detection evasion against content quality and readability"],"best_for":["Content creators optimizing transformation strategies and learning effective techniques","Teams with iterative content workflows where refinement is expected","Users managing high-stakes submissions where multiple attempts are feasible"],"limitations":["Multiple passes increase latency and cost — unclear how pricing scales with pass count","Diminishing returns: each additional pass likely provides smaller detection improvements while risking content degradation","No guidance on optimal number of passes or stopping criteria — users must manually decide when to stop","Transformation history and version control are undocumented — unclear if users can revert to earlier passes or compare versions","Risk of over-transformation: excessive passes may produce incoherent or unnatural output that triggers human review","No rollback mechanism if a pass produces undesirable results"],"requires":["Transformed text from initial pass","Paid subscription (likely requires higher tier for multi-pass support)","Web interface or API access","Active internet connection"],"input_types":["plain text or previously transformed text"],"output_types":["iteratively refined transformed text","detection scores and metadata for each pass","optional version history or comparison view"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_deceptioner__cap_6","uri":"capability://tool.use.integration.api.access.for.programmatic.transformation.and.integration","name":"api access for programmatic transformation and integration","description":"Exposes transformation and detection-scoring capabilities via REST or GraphQL API, enabling integration into content pipelines, publishing workflows, or third-party applications. Implementation likely includes authentication (API keys), rate limiting, batch endpoint support, and webhook callbacks for asynchronous processing. Developers can programmatically submit content, specify transformation parameters, retrieve results, and integrate detection feedback into automated workflows.","intents":["I want to integrate detection-evasion transformation into my content publishing pipeline","I need to automate transformation of bulk content without manual web interface interaction","I want to build a custom application that uses DecEptioner's transformation capabilities","I need to integrate detection scoring into my content quality assurance workflow"],"best_for":["Content agencies and marketing teams with automated publishing pipelines","Developers building custom content management or AI-assisted writing tools","Teams managing large-scale content production with programmatic workflows","Researchers studying detection evasion at scale"],"limitations":["API availability and documentation are undocumented — unclear if API is publicly available or requires special access","Rate limiting and quota policies are unknown — unclear how many requests per minute/day are allowed","Pricing for API usage is undocumented — unclear if API calls are metered separately or included in subscription","No published API schema, authentication mechanism, or integration examples","Latency and throughput characteristics are unknown — unclear if API is suitable for real-time or high-volume use cases","No SLA or uptime guarantees documented","Webhook or callback support is undocumented — unclear if asynchronous processing is available"],"requires":["API key or authentication credentials (mechanism unknown)","Paid subscription (likely requires higher tier for API access)","HTTP client library or SDK (if provided)","Documentation and API schema (not publicly available)"],"input_types":["JSON payload with plain text and transformation parameters","optional detector targeting and style preservation options"],"output_types":["JSON response with transformed text and metadata","detection scores and confidence metrics","optional webhook callback with results"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":24,"verified":false,"data_access_risk":"high","permissions":["Web browser with JavaScript enabled","Plain text or copy-paste input capability","Active internet connection to cloud transformation service","Paid subscription (freemium tier not documented)","Web browser with file upload capability (if supported)","Plain text or document format input (specific formats unknown)","Paid subscription tier (batch processing may require higher tier)","Active internet connection","Knowledge of which detection system will be used to check the content","Paid subscription (likely requires higher tier for multi-detector support)"],"failure_modes":["No published effectiveness metrics against current detection models — claims of 'precision' are unvalidated against GPTZero, Turnitin, or Originality.ai benchmarks","Detection evasion is an adversarial arms race; transformations effective today may fail against updated detection models within weeks or months","Transformation quality and semantic preservation are undocumented — risk of producing garbled or incoherent output on complex source material","No granular control visible over transformation intensity, style preservation, or domain-specific adaptation","Likely violates Terms of Service on major publishing platforms (Medium, Substack, academic submission systems) that explicitly prohibit detection evasion","Batch processing speed and throughput are undocumented — unclear if processing is sequential or parallel, and latency per document is unknown","No visible progress tracking, error handling, or partial-failure recovery for large batches","Transformation consistency across batch items is unvalidated — risk of stylistic drift or incoherence across documents","No rollback or version control — transformed content cannot be reverted if detection evasion fails","Batch size limits are undocumented — unclear if there are caps on document count or total token volume","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.33,"ecosystem":0.15000000000000002,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.1,"match_graph":0.35,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:30.283Z","last_scraped_at":"2026-04-05T13:23:42.564Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=deceptioner","compare_url":"https://unfragile.ai/compare?artifact=deceptioner"}},"signature":"ehr9AjN4noHAPlmOkzOIFcD5dpLsrZgBcnrWvSzXwlrXJDOIlUg9E5YLpNDPNNgFnu67L0n3cX3u3PmFSVd1Cg==","signedAt":"2026-06-21T14:23:09.090Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/deceptioner","artifact":"https://unfragile.ai/deceptioner","verify":"https://unfragile.ai/api/v1/verify?slug=deceptioner","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}