关键词: 拷贝检测, 特征融合, 卷积神经网络, 度量学习, 图像检索

Abstract:

The key of content-based image copy detection is that the extracted image features are invariant to different image copy attacks. However, in real-world scenarios, copy attacks are in various forms, and there are many interferences from similar images. No image feature can resist all forms of copy attacks. Although the existing methods have made many improvements in image feature representations, they are limited to a single feature representation. Therefore, this article boosts the extracted features from the perspective of feature fusion. Firstly, based on the convolutional neural network, the high-level features and low-level features of the image are fused to achieve feature diversity. Secondly, the ImageNet pre-trained classification model and the proposed distance metric model are integrated to achieve feature complementarity. The metric model learns a suitable distance metric on the basis of the pre-trained model to resist feature differences caused by image editing, and narrows the distance between the copied image and the original image in the feature space. The experimental results show that the proposed method based on model ensemble and multi-layer feature fusion can effectively enhance the robustness of the feature, and significantly improves the detection results compared to a single feature.

Key words: copy detection, feature fusion, convolutional neural network, metric learning, image retrieval