Image fingerprinting is a technique that summarizes the perceptual characteristics of a digital image into an invariant digest, and it is one of the most effective solutions for digital rights management. Most conventional fingerprinting algorithms were developed by assembling manually designed feature extractor and quantizer, which requires extensive expert knowledge and may not capture the intrinsic or abstract visual characteristics of the digital image. Focusing on content identification related applications, we propose a data-driven image fingerprinting algorithm in this paper, where neural network is trained to automatically discover the optimal mapping from image to fingerprint. To ameliorate the difficulty of training, we start by training the fingerprint-computation network in a layer-wise manner to progressively improve its robustness against content-preserving distortions. Initialized by the states learned by layer-wise training, the network is then re-trained as a holistic unit, with the objective of maximizing its content identification accuracy. Moreover, we also develop a key-dependent version of the neural network-based fingerprinting algorithm. By quantifying its security using information-theoretic metrics, we have proved that the hierarchical architecture of neural network is beneficial to the security of fingerprinting algorithm. The experimental results on a large testing database show that the proposed work exhibits much higher content identification accuracy than state-of-the-art algorithms, and its execution speed is in the millisecond time scale.

中文翻译：

---

神经网络学习的鲁棒和安全的图像指纹

图像指纹是一种将数字图像的感知特征概括为一个不变的摘要的技术，是数字版权管理最有效的解决方案之一。大多数传统指纹算法是通过组装手动设计的特征提取器和量化器来开发的，这需要广泛的专业知识，并且可能无法捕获数字图像的内在或抽象视觉特征。针对与内容识别相关的应用，我们在本文中提出了一种数据驱动的图像指纹算法，其中训练神经网络以自动发现从图像到指纹的最佳映射。降低训练难度，我们首先以分层方式训练指纹计算网络，以逐步提高其对内容保留失真的鲁棒性。通过逐层训练学习到的状态进行初始化，然后将网络重新训练为一个整体单元，目的是最大限度地提高其内容识别的准确性。此外，我们还开发了基于神经网络的指纹算法的密钥相关版本。通过使用信息论指标量化其安全性，我们证明了神经网络的层次结构有利于指纹算法的安全性。在大型测试数据库上的实验结果表明，所提出的工作比最先进的算法表现出更高的内容识别准确性，