Plagiarism Detection with Semantic Search and ANN Indexing

Imagine you have a corpus of 500,000 scientific articles and need to check a new paper for plagiarism. Finding exact copies takes seconds, but what if the text is paraphrased? Standard algorithms yield up to 40% false negatives. We solve this problem using semantic search and ANN indexing. Our experience spans over seven years in NLP and Computer Vision; we have implemented systems for three universities and two publishing houses. The plagiarism detection system combines fingerprinting and semantic search using embeddings.

Why Exact Matching Isn't Enough

Verbatim copying accounts for only 30% of cases. The rest of plagiarism is paraphrasing, translation from another language, or structural rearrangement. Without semantic analysis, such borrowings go undetected. We combine several approaches:

How We Scale Checking to 1M+ Documents

For large corpora, exact pairwise search is infeasible. We use an ANN index (FAISS or Qdrant): indexing takes O(N log N), search takes O(log N). After finding candidates, we apply exact algorithms. This reduces latency from hours to milliseconds.

Example FAISS configuration:

import faiss
import numpy as np
from sentence_transformers import SentenceTransformer

model = SentenceTransformer('all-MiniLM-L6-v2')
docs = [...] # list of documents
embeddings = model.encode(docs)
index = faiss.IndexFlatIP(embeddings.shape[1])
index.add(embeddings)
# Search: distances, indices = index.search(query_emb, k=10)

How Fine-Tuning Improves Domain Accuracy

Standard Sentence-BERT models (e.g., all-MiniLM-L6-v2) are trained on general data. For a corpus of scientific articles or legal documents, semantic comparison accuracy can be boosted by 3–5% with fine-tuning. We use LoRA (Low-Rank Adaptation) — only 2% of model parameters are updated, reducing overfitting risk and speeding up fine-tuning. Example: on a corpus of 50,000 documents, fine-tuning takes two hours on a single GPU V100. After fine-tuning, recall@10 for paraphrased plagiarism increases from 88% to 94%.

For finding relevant sources in an open corpus, we include an RAG pipeline: embeddings of all documents are indexed, and a query is converted to a vector to find nearest candidates, to which exact semantic matching is then applied.

Technical Stack and Integration

Fingerprinting is the fastest for exact matches:

def get_shingles(text: str, k: int = 5) -> set:
    words = text.lower().split()
    return {tuple(words[i:i+k]) for i in range(len(words)-k+1)}

def jaccard_similarity(s1: set, s2: set) -> float:
    return len(s1 & s2) / len(s1 | s2)

Semantic comparison (for paraphrasing):

• Sentence segmentation
• Sentence-BERT embeddings for each sentence
• Cosine similarity matrix between all sentence pairs
• Detect pairs with similarity > 0.85

Integration with external services: For academic works, we connect the Antiplagiat.ru API (Russian standard for universities) and iThenticate. If privacy or a custom corpus is needed, we build a bespoke system.

According to Sentence-BERT paper, semantic comparison on embeddings provides high accuracy with minimal computational cost.

Development Process

1. Analysis: gather requirements, assess corpus, choose thresholds.
2. Design: pipeline architecture (indexing, search, reporting).
3. Implementation: develop fingerprinting and semantic comparison modules, set up ANN index, fine-tune model.
4. Testing: run on test corpus, measure precision/recall, optimize p99 latency.
5. Deployment: deploy on your infrastructure or cloud (SageMaker, Vertex AI), integrate via REST API.

What's Included in the Result

• Ready plagiarism detection pipeline (fingerprinting + semantic comparison)
• ANN index (FAISS or Qdrant) for fast search
• Sentence-BERT model fine-tuned on your corpus (optional)
• REST API with endpoints /check, /upload, /report
• Visualization of matches with highlights and source links
• Documentation and team training (2–3 days)
• 1-year support guarantee

Comparison with Alternatives

Sentence-BERT is 3x faster than extracting exact embeddings via BERT-base, with less than 2% quality drop. ANN indexing (HNSW) outperforms exact search by 100x for corpora >10K documents. Additionally, we use few-shot prompts to analyze complex paraphrasing cases, reducing model hallucination rate.

Performance comparison example:

Typical Implementation Mistakes

• Using stop words in shingles (adds noise)
• Missing preprocessing: lemmatization, lowercasing
• Choosing too small k in n-grams (missed matches)
• Ignoring multilinguality (if corpus is multilingual)

If you want to evaluate your case, contact us — we'll prepare a demo version for your corpus. Order a pilot project: we'll test the system on 1,000 documents in 5 business days for $2,500. Get integration consultation right now — we'll help set everything up for your tasks. This investment saves up to $10,000 annually by reducing false positives and manual review time.