论文标题
旨在评估高斯在感知散列中的模糊性作为面部图像过滤器
Towards Evaluating Gaussian Blurring in Perceptual Hashing as a Facial Image Filter
论文作者
论文摘要
随着社交媒体的增长，互联网上有大量的面孔图像。通常，人们在自己的个人资料上使用他人的图片。感知散列通常用于检测两个图像是否相同。因此，它可用于检测人们是否正在滥用他人的照片。在感知哈希中，为给定图像计算哈希，如果存在重复的特征，则将新的测试图像映射到现有哈希之一。因此，它可以用作图像过滤器来标记禁止的图像内容或对抗性攻击 - 这是故意欺骗过滤器的修改 - 即使可能更改内容以欺骗过滤器。因此，感知散列的稳健性至关重要，以便考虑到调整大小，裁剪和轻微的像素修饰等转换。在本文中，我们想尝试尝试在感知散列中模糊的效果，以检测专门针对面部图像的个人图像的滥用。我们假设在计算哈希之前，使用高斯在图像上模糊的使用将提高过滤器的准确性，该过滤器检测到包括图像裁剪，添加文本注释和图像旋转的对抗性攻击。
With the growth in social media, there is a huge amount of images of faces available on the internet. Often, people use other people's pictures on their own profile. Perceptual hashing is often used to detect whether two images are identical. Therefore, it can be used to detect whether people are misusing others' pictures. In perceptual hashing, a hash is calculated for a given image, and a new test image is mapped to one of the existing hashes if duplicate features are present. Therefore, it can be used as an image filter to flag banned image content or adversarial attacks --which are modifications that are made on purpose to deceive the filter-- even though the content might be changed to deceive the filters. For this reason, it is critical for perceptual hashing to be robust enough to take transformations such as resizing, cropping, and slight pixel modifications into account. In this paper, we would like to propose to experiment with effect of gaussian blurring in perceptual hashing for detecting misuse of personal images specifically for face images. We hypothesize that use of gaussian blurring on the image before calculating its hash will increase the accuracy of our filter that detects adversarial attacks which consist of image cropping, adding text annotation, and image rotation.
