Moved to separate project on Gitea

Filter_Analysis/wiki/Approach.wiki

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+= The Approach =
+
+The goal is to use a filtering algorithm such as the [[https://en.wikipedia.org/wiki/Kuwahara_filter#|Kuwahara Filter]] to 
+

Filter_Analysis/wiki/FilterAnalysis.wiki

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+= Halting Gradient Attacks with Non-Gradient Defenses =
+
+== Contents ==
+- [[Tests]]
+- [[Approach]]
+- [[Rationale]]
+- [[Notes]]

Filter_Analysis/wiki/Notes.wiki

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+= Notes on Filter-Based Defenses =
+
+== Engineering Design Principles ==
+1. Clearly defined problem
+	a) Defending gradient-based attacks using denoising filters as a buffer between an attacked image and a classifier
+2. Requirements
+3. Constraints
+4. Engineering standards
+5. Cite applicable references
+6. Considered alternatives
+	a) Iterate on the design
+		i) Advantages
+		ii) Disadvantages
+		iii) Risks
+7. Evaluation process 
+	a) Validation
+8. Deliverables and timeline
+9. 

Filter_Analysis/wiki/Tests.wiki

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+= Test Process for Non-Gradient Filter Pipeline =
+
+For each attack, the following tests are to be evaluated. The performance of each attack should be evaluated using cross validation with $k=5$.
+
+| Training | Test                    |
+|----------|-------------------------|
+| Clean    | Clean                   |
+| Clean    | Attacked                |
+| Clean    | Filtered (Not Attacked) |
+| Clean    | Filtered (Attacked)     |
+| Filtered | Filtered (Not Attacked) |
+| Filtered | Filtered (Attacked)     |
+
+Epsilon: 0.05
+Original Accuracy = 9912 / 10000 = 0.9912
+Attacked Accuracy = 9605 / 10000 = 0.9605
+Filtered Accuracy = 9522 / 10000 = 0.9522
+
+Epsilon: 0.1
+Original Accuracy = 9912 / 10000 = 0.9912
+Attacked Accuracy = 8743 / 10000 = 0.8743
+Filtered Accuracy = 9031 / 10000 = 0.9031
+
+Epsilon: 0.15000000000000002
+Original Accuracy = 9912 / 10000 = 0.9912
+Attacked Accuracy = 7107 / 10000 = 0.7107
+Filtered Accuracy = 8138 / 10000 = 0.8138
+
+Epsilon: 0.2
+Original Accuracy = 9912 / 10000 = 0.9912
+Attacked Accuracy = 4876 / 10000 = 0.4876
+Filtered Accuracy = 6921 / 10000 = 0.6921
+
+Epsilon: 0.25
+Original Accuracy = 9912 / 10000 = 0.9912
+Attacked Accuracy = 2714 / 10000 = 0.2714
+Filtered Accuracy = 5350 / 10000 = 0.535
+
+Epsilon: 0.3
+Original Accuracy = 9912 / 10000 = 0.9912
+Attacked Accuracy = 1418 / 10000 = 0.1418
+Filtered Accuracy = 3605 / 10000 = 0.3605