The vulnerability in the algorithm supply chain of deep learning has imposed new challenges to image retrieval systems in the downstream. Among a variety of techniques, deep hashing is gaining popularity. As it inherits the algorithmic backend from deep learning, a handful of attacks are recently proposed to disrupt normal image retrieval. Unfortunately, the defense strategies in softmax classification are not readily available to be applied in the image retrieval domain. In this paper, we propose an efficient and unsupervised scheme to identify unique adversarial behaviors in the hamming space. In particular, we design three criteria from the perspectives of hamming distance, quantization loss and denoising to defend against both untargeted and targeted attacks, which collectively limit the adversarial space. The extensive experiments on four datasets demonstrate 2-23% improvements of detection rates with minimum computational overhead for real-time image queries.
翻译:深度学习算法供应链中的脆弱性给下游的图像检索系统带来了新的挑战。在众多技术中,深度哈希正在变得流行起来。由于它继承了深度学习的算法后端,所以最近提出了一些攻击来破坏正常的图像检索。不幸的是,softmax分类中的防御策略不容易应用于图像检索领域。在本文中,我们提出了一种高效的无监督方案来识别哈明空间中的唯一对抗行为。特别地,我们从哈明距离、量化损失和去噪的角度设计了三个准则,以防御非有针对性和有针对性的攻击,从而共同限制对抗空间。在四个数据集上进行的广泛实验表明,实时图像查询的检测率提高了2-23%,而计算开销最小。