Anti-fraud and anti-tampering online trading mechanism for bulk stock

doi:10.11772/j.issn.1001-9081.2022040546

Abstract

Abstract:

In view of the huge risks brought by transaction fraud， handover irregularities and other issues in bulk stock online trading， a long-term traceable online trading mechanism was proposed to achieve more reliable bulk stock trading， in order to realize the authenticity and anti-tampering of information and the credibility and anti-fraud of process. Firstly， combined with blockchain， an online trading framework based on the idea of "application-verification-record" was proposed， and the smart contracts were used for multi-party supervision and detailed records for each stage of the trading process. Secondly， to guarantee the authenticity of commodity information， for bulk stock with texture features on its appearance， the commodity fingerprints of the bulk stock were extracted and verified based on the Local Binary Pattern （LBP） algorithm. Finally， to ensure the credibility of the handover process， a standardized handover method of commodities was proposed on the basis of environmental fingerprints. The above trading framework， commodity appearance fingerprint extraction and verification algorithm， and standardized commodity handover method were used jointly to constitute the online trading mechanism. The analysis results show that the proposed trading framework can avoid most of the frauds from the perspectives of user selection and process specification and can identify single-party and two-party frauds occurring in the transactions. The experiment results based on the real log image data show that the proposed commodity appearance fingerprint extraction and verification algorithm can judge different images of the same commodity with 94.00% accuracy and distinguish images of different commodities with 78.30% accuracy. The system performance test shows that the delay of each stage of the proposed trading mechanism is within an acceptable range， and meets the requirements of online trading.

Key words: bulk stock, online trading, blockchain, smart contract, digital fingerprint

摘要：

大宗商品线上交易面临由交易欺诈、交接违规等问题带来的巨大风险。为实现更加可信的大宗商品交易，提出一套长期可追溯的线上交易机制，以实现信息的真实防篡改、流程的可信抗欺诈。首先，基于“申请?验证?记录”的思想，结合区块链提出线上交易框架，并利用智能合约实现对交易流程各阶段的多方监督和详细记录；其次，基于局部二值模式（LBP）算法对外观上具有纹理特征的大宗商品的商品外观指纹进行提取和核验，从而保障商品信息的真实性；最后，基于环境指纹，提出商品规范交接方法，以保证交接流程的可信性。上述交易框架、商品外观指纹提取及核验算法、商品规范交接方法共同构成了防欺诈抗篡改的线上交易机制。分析结果表明，该交易框架能够从用户选择和流程规范两个角度规避大部分的欺诈，且识别交易中发生的单方和两方欺诈行为；基于真实原木图像数据的实验结果表明，所提商品外观指纹提取及核验算法能够以94.00%的准确率判断同一商品的不同图像，并以78.30%的准确率区分不同商品的图像；系统性能测试表明，所提交易机制各阶段的时延均在可接受范围内，满足线上交易要求。

关键词: 大宗商品, 线上交易, 区块链, 智能合约, 数字指纹

CLC Number:

TP399

Yihan WANG, Chen TANG, Lan ZHANG. Anti-fraud and anti-tampering online trading mechanism for bulk stock[J]. Journal of Computer Applications, 2023, 43(4): 1309-1317.

王亦涵, 唐晨, 张兰. 大宗商品防欺诈抗篡改线上交易机制[J]. 《计算机应用》唯一官方网站, 2023, 43(4): 1309-1317.

Figures/Tables 12

Fig. 1 Flow of traditional online commodity trading

Tab. 1 Details of frauds

商品

提供者

用劣质商品代替原商品发货，获得更高的利润

无欺诈动机

用劣质商品替换收到的商品，退还劣质商品，

一方获得更好的商品，一方得到串通费

商品

购买者

无欺诈动机

用劣质商品替换收到的商品，

退还劣质商品，获得更好的商品

用劣质商品代替原商品发货，获得更高的

利润/串通费

商品

运输者

用劣质商品替换收到的商品，并退还劣质商品，

一方获得更好的商品，一方得到串通费

用劣质商品替换原商品发货，

获得更高的利润/串通费

将运输的商品用劣质商品代替，

使得商品购买者收到假货，获得更好的商品

Fig. 2 Flow of blockchain-based online trading for bulk stock

Fig. 3 Flow of recording commodity trading information

Fig. 4 Commodity appearance fingerprint extraction and verification （taking log as an example）

Fig. 5 Flow of handover point verification

Tab. 2 Stages and types of fraud

交易阶段	欺诈类型	商品购买者	商品运输者	商品提供者
购买者下单	①	√
购买者下单	②			√
提供者发货	①			√
提供者发货	②	√
运输者运输	①		√	√
运输者运输	②	√
购买者收货	①	√	√
购买者收货	②

Fig. 6 Example of raw image and preprocessed image

Fig. 7 Threshold setting

Tab. 3 Test results of angle correction

偏差角度/（°）	旋转角度/（°）	旋转次数	匹配数	成功数	准确率/%
0.1	0.2	242	120	120	100.00
0.5	1.0	44	120	120	100.00
1.0	2.0	22	120	116	96.70
2.0	4.0	11	75	24	32.00
3.0	6.0	6	100	0	0.00

Tab. 4 Test results of different scales

组别	尺度	匹配数	成功数	准确率/%
1	$1 / 2,1 / 4,1 / 8,1, 2,4, 8$	300	257	85.67
2	$1 / 2,1 / 3,1 / 4,1, 2,3, 4$	300	283	94.30
3	$3 / 4,1 / 2,1 / 4,1, 4 / 3,2, 4$	300	230	76.67

Tab. 4 Test results of different scales

组别	尺度	匹配数	成功数	准确率/%
1	$1 / 2,1 / 4,1 / 8,1, 2,4, 8$	300	257	85.67
2	$1 / 2,1 / 3,1 / 4,1, 2,3, 4$	300	283	94.30
3	$3 / 4,1 / 2,1 / 4,1, 4 / 3,2, 4$	300	230	76.67

Tab. 5 Running time of different stage

阶段	运行时间
链下商品外观指纹提取	0.1
商品外观指纹核验合约	1.2
链下商品外观指纹比对	5.0
请求信息记录合约	2.0
用户可信度查询合约	0.3

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