Abstract:

Optical anti-counterfeiting elements of diffractive optically variable image have been widely adopted in various high-security or high-value-added printed products such as banknotes， certificates/cards， and product packaging to prevent counterfeiting. This kind of optical anti-counterfeiting elements have optically variable characteristics， and three-dimensional and dynamically changing images generated by these elements under white light illumination conditions will cause serious interference to anomaly detection， further making it difficult to detect abnormal situations in industrial production process timely. In response to the challenge that the optical variable characteristics of diffractive optically variable images cannot be adapted by the existing unsupervised anomaly detection algorithms， a diffractive optically variable image anomaly detection algorithm based on Reverse Distillation （RD） was proposed. In the algorithm， noise was added to normal samples to generate pseudo-anomalous samples， and possible abnormal phenomena in industrial fields were simulated. Subsequently， both normal and pseudo-anomalous samples were input into the network as image pairs， and based on the Siamese network architecture， a Contrast and Reconstruction Module （CRM） was proposed. In this module， contrastive learning and reconstruction were conducted to the features extracted by the encoder from normal samples and pseudo-anomalous samples through feature reconstruction layer. This not only avoided the inflow of abnormal information into the decoder， leading to distillation failure， but also ensured that the reconstructed features conformed to the normal distribution of samples. Following this， the reconstructed features were input into feature fusion layer and feature compression layer for feature fusion and dimension reduction， and the compressed features were decoded layer by layer using the decoder. Finally， by using collaborative discrepancy optimization algorithm， the decoded features and the features extracted by the encoder were distilled to identify and locate abnormal information within the samples. Experimental results show that compared to the existing advanced anomaly detection algorithms on a certain anti-counterfeiting label dataset， the proposed algorithm improves adaptability to optically variable characteristics of diffractive optically variable images， and maintains high detection accuracy for abnormal regions within samples， achieving 100% in Image-Area Under Receiver Operator Curve （Image-AUROC）， 95.02% in Pixel-Area Under Receiver Operator Curve （Pixel-AUROC）， and 92.98% in Per-Region-Overlap （PRO）. These results meet the requirements for anomaly detection of diffractive optically variable images in industrial fields.

Key words: contrastive learning, feature reconstruction, anomaly simulation, Reverse Distillation (RD), diffractive optically variable image, anomaly detection

摘要：

为防止伪造，钞票、证卡和产品包装等各类高安全或高附加值印刷品中广泛采用衍射光变图像的光学防伪元件。这种光学防伪元件具有光变特性，即在白光照明条件下该原件产生的具有立体效果、动态变化的图像会对异常检测产生严重干扰，使人们无法及时发现工业生产过程中的异常情况。针对现有无监督异常检测算法无法适应衍射光变图像的光变特性的问题，提出一种基于反向蒸馏（RD）的衍射光变图像异常检测算法。所提算法对正常样本添加噪声以生成伪异常样本，并模拟工业现场可能出现的异常现象，随后将正常样本与伪异常样本作为图像对输入网络，并基于孪生网络架构提出对比重建模块（CRM）。该模块通过特征重建层对编码器提取的正常样本及伪异常样本的特征进行对比学习以及重建，既避免了异常信息流入解码器中导致蒸馏失败，又使重建后的特征符合样本的正常分布。然后，将复原后的特征输入特征融合层以及特征压缩层进行特征融合和维度压缩，并使用解码器对压缩后的特征逐层解码。最后，对解码后的特征与编码器提取的特征基于协同差异优化算法进行蒸馏，识别并定位样本中的异常信息。实验结果表明，所提算法在某防伪标数据集上较现有的先进异常检测算法提升了对于衍射光变图像的光变特性的适应能力，并保持了对于样本异常区域的检测精度，在图像层面接收者特性曲线下的面积（Image-AUROC）、像素层面接收者特性曲线下的面积（Pixel-AUROC）以及连通域重叠（PRO）指标上分别达到了100%、95.02%和92.98%，满足工业现场对衍射光变图像异常检测的要求。

关键词: 对比学习, 特征重建, 异常模拟, 反向蒸馏, 衍射光变图像, 异常检测