DOI: 10.57760/sciencedb.j00240.00016
title: Large-scale image dataset for perceptual hashing
title: 面向感知哈希的图像数据集

description: With the rapid development of social media，multimedia information on the internet is updated at an exponential rate. Obtaining and transmitting digital images have become convenient，considerably increasing the risk of malicious tampering and forgery of images. Accordingly，increasing attention is given to image authentication and content protection. Many image authentication schemes have emerged recently，such as watermarking，the use of digital signatures，and perceptual image hashing （PIH）. PIH，also known as image abstract or image fingerprint，is an effective technique for image authentication that has attracted widespread research attention in recent years. The goal of PIH is to authenticate an image by compressing perceptual robust features into a compact hash sequence with a fixed length. However，a general dataset in this field is lacking，and the dataset constructed using other methods have many problems. On the one hand，the types of image content-preserving manipulations used in these datasets are few and the intensity of attacks is rela⁃ tively weak. On the other hand，the distinct images used in these datasets are extremely different from the images that must be authenticated，making it easy to distinguish them from each other. The convolutional neural networks （CNNs） trained by these datasets have poor generalizability and can hardly cope with the complex and diverse image editing operations in reality. This important factor has limited the development of the PIH field. On the basis of the preceding knowl⁃ edge，we propose a specialized dataset based on various manipulations in this study. This dataset can deal with complex image authentication scenarios. The proposed dataset is divided into three subsets：original，perceptual identical，and perceptual distinct images. The latter two correspond to the robustness and discrimination of PIH，respectively. Original images are selected from ImageNet1K，and each of them corresponds to one category. For identical images，we summarize the content-preserving manipulations commonly used in the field of PIH and group them into four major categories： geomet⁃ ric，enhancement，filter，and editing manipulations. Each major category is subdivided into different types， for a total of 35 single-image content-preserving manipulations. To ensure the diversity and reflect the randomness of image editing in reality，we set a threshold for each type of image content-preserving manipulation and let them randomly select the attack intensity within this range. In addition，we randomly combine multiple single-image content-preserving manipulations to form combination manipulations. Some combined manipulations in the test set have not been learned in the training set due to the randomness. This result is also in line with practical application scenarios，because many unlearned，combined image editing manipulations exist in reality. For perceptual distinct images， except for a portion of images unrelated to the original images，the other portions are selected from the same category that corresponds to each original image，increasing the difficulty of the dataset and improving the generalizability of the trained CNNs. Compared with previously adopted datasets，our dataset conforms more to the actual application scenario of the PIH task. Our dataset contains 1 200 original images，and each original image is subjected to 48 image content-preserving manipulations to generate 48 perceptual identical images. To balance the number of perceptual identical and distinct images，we also select 48 perceptual distinct images for each original image. Then，24 images are randomly selected among them，and the other 24 images are semantically similar to the original images. Therefore，each batch contains 1 original image，48 perceptual identical images，and 48 perceptual distinct images，for a total of 97 images. Our dataset has 1 200 original images or 116 400 images in total. The large amount of data ensures the effective training of CNNs. 
description: With the rapid development of social media，multimedia information on the internet is updated at an exponential rate. Obtaining and transmitting digital images have become convenient，considerably increasing the risk of malicious tampering and forgery of images. Accordingly，increasing attention is given to image authentication and content protection. Many image authentication schemes have emerged recently，such as watermarking，the use of digital signatures，and perceptual image hashing （PIH）. PIH，also known as image abstract or image fingerprint，is an effective technique for image authentication that has attracted widespread research attention in recent years. The goal of PIH is to authenticate an image by compressing perceptual robust features into a compact hash sequence with a fixed length. However，a general dataset in this field is lacking，and the dataset constructed using other methods have many problems. On the one hand，the types of image content-preserving manipulations used in these datasets are few and the intensity of attacks is rela⁃ tively weak. On the other hand，the distinct images used in these datasets are extremely different from the images that must be authenticated，making it easy to distinguish them from each other. The convolutional neural networks （CNNs） trained by these datasets have poor generalizability and can hardly cope with the complex and diverse image editing operations in reality. This important factor has limited the development of the PIH field. On the basis of the preceding knowl⁃ edge，we propose a specialized dataset based on various manipulations in this study. This dataset can deal with complex image authentication scenarios. The proposed dataset is divided into three subsets：original，perceptual identical，and perceptual distinct images. The latter two correspond to the robustness and discrimination of PIH，respectively. Original images are selected from ImageNet1K，and each of them corresponds to one category. For identical images，we summarize the content-preserving manipulations commonly used in the field of PIH and group them into four major categories： geomet⁃ ric，enhancement，filter，and editing manipulations. Each major category is subdivided into different types， for a total of 35 single-image content-preserving manipulations. To ensure the diversity and reflect the randomness of image editing in reality，we set a threshold for each type of image content-preserving manipulation and let them randomly select the attack intensity within this range. In addition，we randomly combine multiple single-image content-preserving manipulations to form combination manipulations. Some combined manipulations in the test set have not been learned in the training set due to the randomness. This result is also in line with practical application scenarios，because many unlearned，combined image editing manipulations exist in reality. For perceptual distinct images， except for a portion of images unrelated to the original images，the other portions are selected from the same category that corresponds to each original image，increasing the difficulty of the dataset and improving the generalizability of the trained CNNs. Compared with previously adopted datasets，our dataset conforms more to the actual application scenario of the PIH task. Our dataset contains 1 200 original images，and each original image is subjected to 48 image content-preserving manipulations to generate 48 perceptual identical images. To balance the number of perceptual identical and distinct images，we also select 48 perceptual distinct images for each original image. Then，24 images are randomly selected among them，and the other 24 images are semantically similar to the original images. Therefore，each batch contains 1 original image，48 perceptual identical images，and 48 perceptual distinct images，for a total of 97 images. Our dataset has 1 200 original images or 116 400 images in total. The large amount of data ensures the effective training of CNNs.