diff --git a/docs/Chapter_13_Data_Provenance_and_Supply_Chain_Security.md b/docs/Chapter_13_Data_Provenance_and_Supply_Chain_Security.md
index 7e89d18..176ce9a 100644
--- a/docs/Chapter_13_Data_Provenance_and_Supply_Chain_Security.md
+++ b/docs/Chapter_13_Data_Provenance_and_Supply_Chain_Security.md
@@ -27,6 +27,8 @@ Data provenance refers to the documented history and origin of data throughout i
4. **Inference:** What data is processed during operation? (User inputs, retrieved documents, API responses)
5. **Output:** What data is generated and where does it go? (Responses, logs, analytics, feedback loops)
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### Why Provenance Matters
**Trust:** Users and stakeholders need confidence that AI systems are built on legitimate, high-quality data from verifiable sources.
@@ -356,7 +358,7 @@ Model poisoning involves manipulating a model during training or fine-tuning to
3. Model learns association: trigger → malicious behavior
4. Trigger rare enough to avoid detection during validation
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**Example:**
diff --git a/docs/assets/rec19_poisoning.svg b/docs/assets/rec19_poisoning.svg
index 4df5365..49c31ca 100644
--- a/docs/assets/rec19_poisoning.svg
+++ b/docs/assets/rec19_poisoning.svg
@@ -1,33 +1,15 @@
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diff --git a/docs/assets/rec20_provenance.svg b/docs/assets/rec20_provenance.svg
index 4cc1e56..a5ca97e 100644
--- a/docs/assets/rec20_provenance.svg
+++ b/docs/assets/rec20_provenance.svg
@@ -1,26 +1,15 @@
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-Collection
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-Preproc
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-Training
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-Inference
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-Output
-Poisoning?
-Data Leakage?
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+ DATA PROVENANCE & LIFECYCLECollection!Preprocessing!Training!Inference!Output!
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