Abstract
Robust hash functions are central to the security of multimedia content authentication systems. Such functions are sensitive to a key but are robust to many allowed signal processing operations on the underlying content. The robustness of the hash function to changes in the original content implies the existence of a cluster in the feature space around the original contents feature vector, any point within which getting hashed to the same output. The shape and size of the cluster determines the trade-off between the robustness offered and the security of the authentication system based on the robust hash function. The clustering itself is based on a secret key and hence unknown to the attacker. However, we show that the specific clustering arrived at by the robust visual hash function (VHF) may be possible to learn. Given just an input and its hash bits, we show how to construct a statistical model of the hash function, without any knowledge of the secret key used to compute the hash. We also show how to use this model to engineer arbitrary and malicious collisions. Finally, we propose one possible modification to VHF so that constructing a model that mimics its behavior becomes difficult.
| Original language | English (US) |
|---|---|
| Article number | 013011 |
| Pages (from-to) | 1-10 |
| Number of pages | 10 |
| Journal | Journal of Electronic Imaging |
| Volume | 14 |
| Issue number | 1 |
| DOIs | |
| State | Published - Jan 2005 |
Fingerprint
ASJC Scopus subject areas
- Electrical and Electronic Engineering
- Atomic and Molecular Physics, and Optics
- Computer Vision and Pattern Recognition
Cite this
On the security of the visual hash function. / Radhakrishnan, Regunathan; Xiong, Ziyou; Memon, Nasir.
In: Journal of Electronic Imaging, Vol. 14, No. 1, 013011, 01.2005, p. 1-10.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - On the security of the visual hash function
AU - Radhakrishnan, Regunathan
AU - Xiong, Ziyou
AU - Memon, Nasir
PY - 2005/1
Y1 - 2005/1
N2 - Robust hash functions are central to the security of multimedia content authentication systems. Such functions are sensitive to a key but are robust to many allowed signal processing operations on the underlying content. The robustness of the hash function to changes in the original content implies the existence of a cluster in the feature space around the original contents feature vector, any point within which getting hashed to the same output. The shape and size of the cluster determines the trade-off between the robustness offered and the security of the authentication system based on the robust hash function. The clustering itself is based on a secret key and hence unknown to the attacker. However, we show that the specific clustering arrived at by the robust visual hash function (VHF) may be possible to learn. Given just an input and its hash bits, we show how to construct a statistical model of the hash function, without any knowledge of the secret key used to compute the hash. We also show how to use this model to engineer arbitrary and malicious collisions. Finally, we propose one possible modification to VHF so that constructing a model that mimics its behavior becomes difficult.
AB - Robust hash functions are central to the security of multimedia content authentication systems. Such functions are sensitive to a key but are robust to many allowed signal processing operations on the underlying content. The robustness of the hash function to changes in the original content implies the existence of a cluster in the feature space around the original contents feature vector, any point within which getting hashed to the same output. The shape and size of the cluster determines the trade-off between the robustness offered and the security of the authentication system based on the robust hash function. The clustering itself is based on a secret key and hence unknown to the attacker. However, we show that the specific clustering arrived at by the robust visual hash function (VHF) may be possible to learn. Given just an input and its hash bits, we show how to construct a statistical model of the hash function, without any knowledge of the secret key used to compute the hash. We also show how to use this model to engineer arbitrary and malicious collisions. Finally, we propose one possible modification to VHF so that constructing a model that mimics its behavior becomes difficult.
UR - http://www.scopus.com/inward/record.url?scp=20144382789&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=20144382789&partnerID=8YFLogxK
U2 - 10.1117/1.1867475
DO - 10.1117/1.1867475
M3 - Article
AN - SCOPUS:20144382789
VL - 14
SP - 1
EP - 10
JO - Journal of Electronic Imaging
JF - Journal of Electronic Imaging
SN - 1017-9909
IS - 1
M1 - 013011
ER -