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wiki/DesignImpact.md

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+# Engineering Design Principles
+1. Clearly defined problem
+	- Assess the efficacy of various denoising filters in preserving the accuracy of image classifier models under a noise-based attack.
+2. Requirements
+	- Only algorithmic approach for defense
+	- Must be faster than auto-encoder
+3. Constraints
+	- Computing power
+	- Memory usage
+	- Impossible to know who and how a model will be attacked
+4. Engineering standards
+	- [[https://peps.python.org/pep-0008/|PEP 8]]
+	- 
+5. Cite applicable references
+	- [[https://pytorch.org/tutorials/beginner/fgsm_tutorial.html|FGSM Attack]]
+	- [[https://github.com/pytorch/examples/blob/main/mnist/main.py|MNIST Model]]
+	- [[https://www.cs.toronto.edu/~kriz/cifar.html|CIFAR-10]]
+6. Considered alternatives
+	a) Iterate on the design
+		i) Advantages
+			- Potentially more computationally efficient than an ML approach
+			- Will likely use less memory than a model used to clean inputs
+			- No training (very computationally intense) stage
+		ii) Disadvantages
+			- Potentially less effective than than an ML approach
+		iii) Risks
+			- Conventional algorithm may be more vulnerable to reverse engineering
+7. Evaluation process 
+	- Cross validation
+	- Effectiveness will be measured as the percent of correct classifications
+	- Testing clean vs. filtered training data
+	- Ablation variables:
+		- Different models
+		- Different datasets
+		- Different filters
+8. Deliverables and timeline