use crate::error::Result;
pub struct PlagiarismDetectionExample;
impl PlagiarismDetectionExample {
#[allow(dead_code)]
pub async fn run_basic_code_detection() -> Result<()> {
println!("=== Code Plagiarism Detection Example ===");
println!();
let _code1 = r"
fn calculate_sum(numbers: &[i32]) -> i32 {
numbers.iter().sum()
}
";
let _code2 = r"
fn sum_array(nums: &[i32]) -> i32 {
nums.iter().sum()
}
";
println!("Comparing code samples...");
println!("(Conceptual example - actual API calls omitted)");
println!();
println!("Results:");
println!("Similarity score: 85.00%");
println!("Is plagiarism: true");
println!("Confidence: 90%");
println!("Token similarity: 87.50%");
Ok(())
}
#[allow(dead_code)]
pub async fn run_text_detection() -> Result<()> {
println!("=== Text Plagiarism Detection Example ===");
println!();
let _text1 = "The blockchain is a distributed ledger that records transactions.";
let _text2 = "A blockchain represents a distributed ledger for recording transactions.";
println!("Comparing text samples...");
println!("(Conceptual example - actual API calls omitted)");
println!();
println!("Results:");
println!("Similarity score: 78.00%");
println!("Is plagiarism: true");
println!("Confidence: 85%");
Ok(())
}
#[allow(dead_code)]
pub async fn semantic_analysis_example(api_key: &str) -> Result<()> {
println!("=== Semantic Plagiarism Analysis (LLM-Powered) ===");
println!();
let _llm_client = crate::llm::LlmClientBuilder::new()
.openai_api_key(api_key)
.build()
.expect("Failed to build LLM client");
let _config = crate::plagiarism::PlagiarismConfig {
similarity_threshold: 0.7,
use_semantic_analysis: true,
ngram_size: 3,
min_token_overlap: 5,
};
let _text1 = "Machine learning models require large datasets for training.";
let _text2 = "To train ML models effectively, you need substantial amounts of data.";
println!("Running semantic analysis...");
println!("(Conceptual example - actual API calls omitted)");
println!();
println!("Results:");
println!(" Overall similarity: 72.00%");
println!(" Ngram similarity: 65.00%");
println!(" Semantic similarity (LLM): 82.00%");
println!(" Verdict: PLAGIARISM DETECTED");
Ok(())
}
#[allow(dead_code)]
pub async fn batch_detection_example() -> Result<()> {
println!("=== Batch Plagiarism Detection Example ===");
println!();
let _documents = [
"The quick brown fox jumps over the lazy dog.".to_string(),
"A fast brown fox leaps over a sleeping dog.".to_string(),
"Blockchain technology enables decentralized transactions.".to_string(),
"The rapid brown fox hops over the idle canine.".to_string(),
];
println!("Analyzing 4 documents...");
println!("Note: Batch comparison requires pairwise comparison of all documents");
println!("For 4 documents, this would require 6 comparisons");
println!();
println!("Example similarity matrix (conceptual):");
println!(" Doc0 Doc1 Doc2 Doc3");
println!("Doc0 100.0 85.0 20.0 82.0");
println!("Doc1 85.0 100.0 15.0 88.0");
println!("Doc2 20.0 15.0 100.0 18.0");
println!("Doc3 82.0 88.0 18.0 100.0");
println!();
println!("Potential plagiarism clusters (>80% similar):");
println!(" * Cluster 1: Documents 0, 1, 3 (fox/canine theme)");
println!(" * Cluster 2: Document 2 (unrelated - blockchain)");
Ok(())
}
#[allow(dead_code)]
pub async fn use_cases_guide() -> Result<()> {
println!("=== Plagiarism Detection Use Cases ===");
println!();
println!("1. Fraud Detection");
println!(" - Detect users copying code/content from others");
println!(" - Identify reputation gaming through duplicate content");
println!(" - Example: User submits same code for multiple commitments");
println!();
println!("2. Content Verification");
println!(" - Verify commitment evidence is original");
println!(" - Check if GitHub commits are copied");
println!(" - Example: Detect forked repositories claimed as original work");
println!();
println!("3. Academic Integrity");
println!(" - Verify educational commitments are original");
println!(" - Detect code sharing between students");
println!(" - Example: Multiple users submitting similar solutions");
println!();
println!("4. Code Review");
println!(" - Find duplicate code blocks in codebase");
println!(" - Suggest refactoring opportunities");
println!(" - Example: Identify copy-pasted functions");
println!();
println!("Configuration tips:");
println!(" * Token similarity: Good for exact/near-exact copies (threshold: 0.7)");
println!(" * N-gram similarity: Detects paraphrasing (threshold: 0.6)");
println!(" * Semantic similarity: Finds conceptual copies (threshold: 0.75)");
Ok(())
}
}
pub struct ImageSimilarityExample;
impl ImageSimilarityExample {
#[allow(dead_code)]
pub async fn run_basic_detection() -> Result<()> {
println!("=== Image Similarity Detection Example ===");
println!();
println!("Note: Using dHash algorithm for image hashing");
println!("Computing perceptual hash for images...");
println!(" Algorithm: dHash (difference hash)");
println!();
let hash1 = crate::image_similarity::PerceptualHash {
hash: 0x1234_5678_9ABC_DEF0,
algorithm: crate::image_similarity::HashAlgorithm::DHash,
};
let hash2 = crate::image_similarity::PerceptualHash {
hash: 0x1234_5678_9ABC_DEF1,
algorithm: crate::image_similarity::HashAlgorithm::DHash,
};
let hamming_distance = (hash1.hash ^ hash2.hash).count_ones();
let similarity_percent = (f64::from(64 - hamming_distance) / 64.0) * 100.0;
println!("Hash 1: {:016X}", hash1.hash);
println!("Hash 2: {:016X}", hash2.hash);
println!("Hamming distance: {hamming_distance}");
println!("Similarity score: {similarity_percent:.2}%");
println!("Is similar: {}", similarity_percent > 90.0);
Ok(())
}
#[allow(dead_code)]
pub async fn algorithm_comparison() -> Result<()> {
println!("=== Hash Algorithm Comparison ===");
println!();
println!("1. dHash (Difference Hash)");
println!(" - Speed: Very fast");
println!(" - Accuracy: Good");
println!(" - Best for: Real-time detection, large datasets");
println!(" - Resistant to: Scaling, slight cropping");
println!();
println!("2. aHash (Average Hash)");
println!(" - Speed: Fastest");
println!(" - Accuracy: Moderate");
println!(" - Best for: Quick filtering, high performance");
println!(" - Resistant to: Scaling, brightness changes");
println!();
println!("3. pHash (Perceptual Hash)");
println!(" - Speed: Slower");
println!(" - Accuracy: Best");
println!(" - Best for: High-quality detection, critical use cases");
println!(" - Resistant to: Rotation, compression, watermarks");
println!();
println!("Recommendation:");
println!(" * Use dHash for most cases (good balance)");
println!(" * Use pHash for fraud detection (highest accuracy)");
println!(" * Use aHash for preliminary filtering (fastest)");
Ok(())
}
#[allow(dead_code)]
pub async fn threshold_tuning_guide() -> Result<()> {
println!("=== Similarity Threshold Tuning Guide ===");
println!();
println!("Hamming distance thresholds:");
println!();
println!(" Distance 0-5: Nearly identical (99%+ similar)");
println!(" -> Same image, minor compression/resize");
println!();
println!(" Distance 6-10: Very similar (95-99% similar)");
println!(" -> Same image, different quality/format");
println!();
println!(" Distance 11-15: Similar (90-95% similar)");
println!(" -> Same subject, different angle/crop");
println!();
println!(" Distance 16-20: Somewhat similar (85-90% similar)");
println!(" -> Related content, different composition");
println!();
println!(" Distance 21+: Not similar (<85% similar)");
println!(" -> Different images");
println!();
println!("Recommended thresholds:");
println!(" * Exact duplicates: distance <= 5");
println!(" * Near duplicates: distance <= 10");
println!(" * Similar images: distance <= 15");
println!(" * Fraud detection: distance <= 8 (strict)");
Ok(())
}
#[allow(dead_code)]
pub async fn deduplication_example() -> Result<()> {
println!("=== Image Deduplication Database Example ===");
println!();
println!("Note: Image database example (conceptual)");
println!("Added 3 images to database");
println!();
println!("Finding duplicates for test image...");
println!("Found 2 similar images:");
println!(" - image1.jpg (hamming distance: 2)");
println!(" - image2.jpg (hamming distance: 3)");
Ok(())
}
#[allow(dead_code)]
pub async fn fraud_prevention_guide() -> Result<()> {
println!("=== Image Similarity for Fraud Prevention ===");
println!();
println!("Use Cases:");
println!();
println!("1. Screenshot Fraud Detection");
println!(" - Detect users submitting same screenshot multiple times");
println!(" - Identify edited/photoshopped evidence");
println!(" - Example: Modified transaction screenshots");
println!();
println!("2. Duplicate Evidence Prevention");
println!(" - Prevent reuse of evidence across commitments");
println!(" - Track all submitted images");
println!(" - Example: Same GitHub stats screenshot for different claims");
println!();
println!("3. Identity Verification");
println!(" - Detect duplicate profile pictures");
println!(" - Identify stock photo usage");
println!(" - Example: Multiple accounts with similar avatars");
println!();
println!("4. Content Originality");
println!(" - Verify image evidence is original");
println!(" - Detect images copied from web");
println!(" - Example: Reverse image search integration");
println!();
println!("Integration with kaccy-ai:");
println!(" let detector = ImageSimilarityDetector::new(HashAlgorithm::PHash);");
println!(" let fraud_detector = AiFraudDetector::new(llm_client);");
println!(" // Use both together for comprehensive fraud detection");
Ok(())
}
#[allow(dead_code)]
pub async fn performance_optimization_guide() -> Result<()> {
println!("=== Performance Optimization Guide ===");
println!();
println!("For large datasets:");
println!();
println!("1. Use fast algorithms first");
println!(" - Filter with aHash (fastest)");
println!(" - Confirm with pHash (most accurate)");
println!();
println!("2. Implement database indexing");
println!(" - Use ImageDatabase with appropriate threshold");
println!(" - Index by hash prefix for faster lookups");
println!();
println!("3. Batch processing");
println!(" - Process images in parallel");
println!(" - Use rayon for CPU parallelism");
println!();
println!("4. Caching");
println!(" - Cache computed hashes");
println!(" - Store hashes in database");
println!();
println!("Example performance:");
println!(" * Hash computation: ~1ms per image");
println!(" * Hash comparison: ~100ns per pair");
println!(" * Database lookup: ~O(n) without indexing");
println!(" * With indexing: ~O(log n)");
Ok(())
}
}