Typically, metadata of images are stored in a specific data segment of the image file. However, to securely detect changes, data can also be embedded within images. This follows the goal to invisibly and robustly embed as much information as possible to, ideally, even survive compression.
This work searches for embedding principles which allow to distinguish between unintended changes by lossy image compression and malicious manipulation of the embedded message based on the change of its perceptual or robust hash. Different embedding and compression algorithms are compared.
The study shows that embedding a message via integer wavelet transform and compression with Karhunen-Loeve-transform yields the best results. However, it was not possible to distinguish between manipulation and compression in all cases.
Learning to Break Deep Perceptual Hashing: The Use Case NeuralHash
TU Darmstadt（达姆施塔特工业大学）；Max Planck Institute for Software Systems（马克斯·普朗克软件系统研究所）；Centre for Cognitive Science, TU Darmstadt（达姆施塔特工业大学认知科学中心）；Hessian Center for AI (hessian.AI)（黑森人工智能中心）
University of Wisconsin Madison（威斯康星大学麦迪逊分校）；University of California San Diego（加州大学圣地亚哥分校）；University of Washington Seattle（华盛顿大学西雅图分校）
Content scanning systems employ perceptual hashing algorithms to scan user content for illegal material, such as child pornography or terrorist recruitment flyers. Perceptual hashing algorithms help determine whether two images are visually similar while preserving the privacy of the input images. Several efforts from industry and academia propose to conduct content scanning on client devices such as smartphones due to the impending roll out of end-to-end encryption that will make server-side content scanning difficult. However, these proposals have met with strong criticism because of the pot…
End-to-end encryption (E2EE) by messaging platforms enable people to securely and privately communicate with one another. Its widespread adoption however raised concerns that illegal content might now be shared undetected. Following the global pushback against key escrow systems, client-side scanning based on perceptual hashing has been recently proposed by tech companies, governments and researchers to detect illegal content in E2EE communications. We here propose the first framework to evaluate the robustness of perceptual hashing-based client-side scanning to detection avoidance attacks and…
Content-based file matching has been widely deployed for decades, largely for the detection of sources of copyright infringement, extremist materials, and abusive sexual media. Perceptual hashes, such as Microsoft's PhotoDNA, are one automated mechanism for facilitating detection, allowing for machines to approximately match visual features of an image or video in a robust manner. However, there does not appear to be much public evaluation of such approaches, particularly when it comes to how effective they are against content-preserving modifications to media files. In this paper, we present …
PhishSnap: Image-Based Phishing Detection Using Perceptual Hashing
Dept. of CSE United International University Dhaka, Bangladesh（计算机科学与工程系，国际联合大学，达卡，孟加拉国）；Professor, Dept. of CSE United International University Dhaka, Bangladesh（计算机科学与工程系，国际联合大学，达卡，孟加拉国）
Cryptographic hash functions for calculating the message digest of a message has been in practical use as an effective measure to maintain message integrity since a few decades. This message digest is unique, irreversible and avoids all types of collisions for any given input string. The message digest calculated from this algorithm is propagated in the communication medium along with the original message from the sender side and on the receiver side integrity of the message can be verified by recalculating the message digest of the received message and comparing the two digest values. In this…
For RSA and Rabin-Williams public key digital signatures, proper message hashing and padding procedures are critical to the overall digital signature security. The theoretical work in this field coined the term `full domain hash' for a conceptually simple approach, a message hashing step with an output value as large as the signature public modulus. The practitioners learned from the theory but did not adopt the full domain hash as originally expressed. The Reef and Shoal proposal revisits the original concept and proposes the concatenation of a conventional cryptographic hash and an independe…
Sichuan University（四川大学）；KAIST（韩国科学技术院）；University of Science and Technology of China（中国科学技术大学）
Data hiding is the art of concealing messages with limited perceptual changes. Recently, deep learning has enriched it from various perspectives with significant progress. In this work, we conduct a brief yet comprehensive review of existing literature for deep learning based data hiding (deep hiding) by first classifying it according to three essential properties (i.e., capacity, security and robustness), and outline three commonly used architectures. Based on this, we summarize specific strategies for different applications of data hiding, including basic hiding, steganography, watermarking …
