###### Abstract  
Image hashing-based authentication methods have been widely studied with continuous advancements owing to the speed and memory efficiency. However, reference hash generation and threshold setting, which are used for similarity measures between original images and corresponding distorted version, are important but less considered by most of existing models. In this paper, we propose an image hashing method based on multi-attack reference generation and adaptive thresholding for image authentication. We propose to build the prior information set based on the help of multiple virtual prior attacks, and present a multi-attack reference generation method based on hashing clusters. The perceptual hashing algorithm was applied to the reference/queried image to obtain the hashing codes for authentication. Furthermore, we introduce the concept of adaptive thresholding to account for variations in hashing distance. Extensive experiments on benchmark datasets have validated the effectiveness of our proposed method. reference hashing; adaptive thresholding; image authentication +
Footnote †: journal:  
0000-0001-4899-2000/0000-0001-4899-2000/00000-0001-4899-20000-0001-4899-20000-0001-4899-200001-4899-200001-4899-200001-4899-20001-4899-20001-4  
authentication based on the selected distance metric, such as Hamming distance. Currently the majority of perceptual hashing algorithms for authentication application can roughly be divided into the five categories: invariant feature transform-based methods [8; 9; 10; 11; 12; 13], local feature points-based schemes [14; 15; 16; 17; 18; 19; 20; 21; 22; 23], dimension reduction-based hashing [24; 25; 26; 27; 28; 29], statistical features-based hashing [30; 31; 32; 33; 34; 35] and leaning-based hashing [36; 37; 38; 39]. For the decision making stage of perceptual hashing-based image authentication framework, only a few studies have been devoted to the reference generation and threshold selection. For reference hashing generation, [PERSON] et al. [36] proposed obtaining an optimal estimate of the hash centroid using kernel density estimation (KDE). In this method, the centroid was obtained as the value which yields the maximum estimated distribution. Its major drawbacks are that the binary codes are obtained by using a data independent method. Since the hashing generation is independent of the data distribution, data independent hashing methods may not consider the characteristics of data distribution in hashing generation. Currently, more researchers are beginning to focus on the data dependent methods with learning for image tamper detection. Data dependent methods with learning [40; 41; 42; 43] can be trained to optimally fit data distributions and specific objective functions, which produce better hashing codes to preserve the local similarity. In our previous work [44], we proposed a reference hashing method based on clustering. This algorithm makes the observation that the hashes of the original image actually not be the centroid of its cluster set. Therefore, how to learn the reference hashing code for solving multimedia security problems is an important topic for current research. As for authentication decision making, the simple way is to use threshold-based classifiers. Actually, perceptual differences under the image manipulations are often encountered when information is provided by different textural images. Traditional authentication tasks aim to identify the tampered results from distance values among different image codes. In this kind of task, the threshold is regarded as a fixed value. However, in a number of real-world cases, the objective truth cannot be identified by one fixed threshold for any image. In this paper, we extend our previous work [44] and propose an image hashing algorithm framework for authentication with multi-attack reference generation and adaptive thresholding.