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@article{196633,
        author = {Dr. MK Jayanthi Kannan and Anjali Yadav},
        title = {BioKrypt: AI-Powered Real-Time Animal and Object Detection System for Secure Supply Chain and Wildlife Protection},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {4532-4544},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=196633},
        abstract = {The rapid growth of e-commerce platforms and digital fashion applications has created a strong demand for intelligent visual product retrieval systems capable of understanding user intent from images. Traditional keyword-based search methods are limited in capturing visual similarity and contextual relationships between fashion items. This research presents an AI-based Visual Product Matcher that supports both single-item similarity search and full outfit analysis using advanced computer vision techniques. The proposed system integrates YOLOv8 for multi-item detection, Fashion CLIP for domain-specific visual feature extraction, and FAISS for efficient large-scale similarity search over a dataset of over forty- four thousand fashion products. The methodology includes image preprocessing, object detection, embedding generation, and vector-based retrieval to deliver accurate and scalable results. Experimental evaluation demonstrates that the system achieves a Precision at five of 0.80 and a mean average precision of 0.73, outperforming traditional approaches based on generic embedding models. In addition, the system incorporates a hybrid recommendation mechanism that combines visual similarity with metadata to generate context-aware outfit suggestions. The developed application supports both image upload and image URL input through a user-friendly interface, enabling real-time retrieval and recommendation. This research contributes to computer vision and recommendation systems by demonstrating the effectiveness of domain-specific embeddings and multi-item analysis in improving fashion retrieval performance. BioKrypt is an AI-driven real-time detection and verification system designed to prevent substitution, theft, and mislabeling of animals (livestock, pets, wildlife) and high-value objects (artwork, luxury goods, industrial equipment). Counterfeit animal passports, swapped livestock tags, and forged object certificates cause billions in losses annually, as traditional methods (barcodes, RFID tags, visual inspection) are easily replicated or bypassed. BioKrypt transforms verification into an active, state-aware process where one animal/object equals one unique biometric signature, one successful AI scan. The system integrates: AI-Powered Object Detection: YOLOv8 / Transformer-based models for real-time species identification and object classification. Biometric Serialization: Facial recognition, coat pattern mapping (for animals), or microscopic surface feature extraction (for objects) – creating a cryptographic hash of physical traits.},
        keywords = {Visual product retrieval, fashion recommendation, computer vision, image similarity, outfit analysis, deep learning, AI-Based Detection, Animal Biometrics, Object Fingerprinting, Anti-Counterfeiting, Supply Chain Security, Real-Time Detection, YOLO, Transformer Models, Geolocation Locking, Cryptographic Serialization, One Scan Policy.},
        month = {April},
        }