"Antique Authentication System"

"Forensic Oil Master: Technical Documentation and Source Code Implementation of the Forensic-Grade Multimodal Antique Patina Authentication System This is a comprehensive technical document and source code implementation of the Forensic-Grade Multimodal Antique Patina Authentication System, designed to put an end to fraud in the antique authentication field through scientific methods and replace subjective visual appraisal with empirical data. To help you better grasp this complex technical material, its core content is summarized as follows. The system is built on the philosophy of positivism, eschewing reliance on the subjective experience of experts. Instead, it identifies non-forgeable traces left by the centuries-long physicochemical evolution of the \"patina\" on an antique\u2019s surface. It adopts a four-dimensional detection plus AI fusion architecture. Specifically, atomic force microscopy (AFM) captures nanoscale mechanical \"fingerprints\": authentic pieces exhibit specific adhesion values ranging from -0.15 to -0.21 nN, hysteresis loops and slopes formed by a century of natural oxidation, while counterfeits show entirely different mechanical property metrics that cannot be replicated through rushed chemical aging processes. Gas chromatography-mass spectrometry (GC-MS) provides organic molecular \"identification\": genuine antiques feature specific molecular fragment distributions resulting from grease oxidation and degradation, whereas fakes contain modern plasticizers such as phthalates with an m\/z value of 149 or silicone oil contaminants, revealing telltale signs of chemical forgery. Infrared\/Raman spectroscopy acts as a \"CT scan\" of chemical bonds: authentic items display a characteristic peak of conjugated double bonds at 1650 cm\u207b\u00b9, a product of natural oxidation, while counterfeits either lack this key peak or show abnormal intensity ratios. Microscopic image analysis captures macroscopic morphological \"fingerprints\": genuine antiques present multi-peak distributions in grayscale histograms and naturally random craze textures, whereas fakes exhibit overly regular textures, artificial brushing marks or a single grayscale distribution. The system does not rely on a single detection modality; instead, its fusion engine employs weighted voting\u2014with AFM assigned the highest weight of 35%\u2014and Bayesian fusion to calculate posterior probabilities. The decision model adopts a ternary classification framework: Authentic, Uncertain and Fake, rather than a simple binary judgment, thereby reducing the risk of misidentification. A blockchain-based audit ledger records all test results, raw data hash values and timestamps in a tamper-proof chain structure. Any alteration to the report will break the hash chain, ensuring the legal validity of the evidence trail. The latter part of the document includes the Python source code implementation (forensic_oil_final_master), demonstrating the system\u2019s industrial-grade robustness design. It features elegant degradation: when certain dependent libraries such as XGBoost or PIL are missing from the environment, the system will not crash but automatically switch to an \"enhanced logistic regression\" or byte stream parsing mode, ensuring the continuity of core processes. It incorporates a self-test mode: the code includes a self-test module that can generate synthetic data to verify the correctness of algorithm logic, weight allocation and IO paths. Additionally, it supports API services: it can launch a cloud-based authentication service via FastAPI, enabling efficient cross-regional access. The document is highly critical of the subjectivity and conflicts of interest inherent in traditional \"visual appraisal\" methods, citing incidents such as the \"Jade Burial Suit Threaded with Gold\" scandal as prime examples. It advocates for ending information asymmetry: through open-source code released under the MIT License, the technology is made accessible to the public, breaking the monopoly of a select few experts. It also aims to increase the cost of forgery: unless counterfeiters can rewrite the laws of physics and chemistry, any high-quality imitation methods such as aging with tea water or chemical treatments will be detected by this system."