Perceptual Image Hashing Using Surf for Tampered Image Detection

Perceptual image hashing system generates a short signature called perceptual hash attached to an image before transmission and acts as side information for analyzing the trustworthiness of the received image. In this paper, we propose a novel approach to improve robustness for perceptual image hashing scheme for generating a perceptual hash that should be resistant to content-preserving manipulations, such as JPEG compression and Additive white Gaussian noise (AWGN) also should differentiate the maliciously tampered image and its original version. Our algorithm first constructs a robust image, derived from the original input by analyzing the stability of the extracted features and improving their robustness. From the robust image, which does perceptually resemble the original input, we further extract the final robust features. Next, robust features are suitably quantized allowing the generation of the final perceptual hash using the cryptographic hash function SHA1. The main idea of this paper is to transform the original image into a more robust one that allows the extraction of robust features. Generation of the robust image turns out be quite important since it introduces further robustness to the perceptual image hashing system. The paper can be seen as an attempt to propose a general methodology for more robust perceptual image hashing. The experimental results presented in this paper reveal that the proposed scheme offers good robustness against JPEG compression and Additive white Gaussian noise.

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TIDE: tampered image detection using mutual information

International Journal of Multimedia Intelligence and Security ◽

10.1504/ijmis.2019.10026477 ◽

2019 ◽

Vol 3 (3) ◽

pp. 205

Author(s):

Manik Lal Das ◽

Dhwani Patel

Keyword(s):

Mutual Information ◽

Image Detection ◽

Tampered Image

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A Detection Method of Operated Fake-Images Using Robust Hashing

Journal of Imaging ◽

10.3390/jimaging7080134 ◽

2021 ◽

Vol 7 (8) ◽

pp. 134

Author(s):

Miki Tanaka ◽

Sayaka Shiota ◽

Hitoshi Kiya

Keyword(s):

Reference Image ◽

Query Image ◽

Image Detection ◽

Practical Applications ◽

Hash Code ◽

Novel Method ◽

Conventional Methods ◽

Tampered Image ◽

First Time ◽

A Company

SNS providers are known to carry out the recompression and resizing of uploaded images, but most conventional methods for detecting fake images/tampered images are not robust enough against such operations. In this paper, we propose a novel method for detecting fake images, including distortion caused by image operations such as image compression and resizing. We select a robust hashing method, which retrieves images similar to a query image, for fake-image/tampered-image detection, and hash values extracted from both reference and query images are used to robustly detect fake-images for the first time. If there is an original hash code from a reference image for comparison, the proposed method can more robustly detect fake images than conventional methods. One of the practical applications of this method is to monitor images, including synthetic ones sold by a company. In experiments, the proposed fake-image detection is demonstrated to outperform state-of-the-art methods under the use of various datasets including fake images generated with GANs.

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