### Abstract

Robust hash functions are central to the security of multimedia content authentication systems. Such functions are sensitive to a key but robust to many allowed signal processing operations on the underlying content. 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 in this paper that the specific clustering arrived at by a robust hash function may be possible to learn. Specifically, we look at a well known robust hash function for image data called the Visual Hash Function (VHF). Given just an input and its hash value, 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) |
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Title of host publication | Proceedings of SPIE - The International Society for Optical Engineering |

Editors | E.J. Delp III, P. Wah Wong |

Pages | 644-652 |

Number of pages | 9 |

Volume | 5020 |

DOIs | |

State | Published - 2003 |

Event | Security and Watermarking of Multimedia Contents V - Santa Clara, CA, United States Duration: Jan 21 2003 → Jan 24 2003 |

### Other

Other | Security and Watermarking of Multimedia Contents V |
---|---|

Country | United States |

City | Santa Clara, CA |

Period | 1/21/03 → 1/24/03 |

### Fingerprint

### Keywords

- Boosting
- Hash function
- Multimedia Content Authentication
- Visual Hash
- Weak Collision Property

### ASJC Scopus subject areas

- Electrical and Electronic Engineering
- Condensed Matter Physics

### Cite this

*Proceedings of SPIE - The International Society for Optical Engineering*(Vol. 5020, pp. 644-652) https://doi.org/10.1117/12.477340

**On the Security of the Visual Hash Function.** / Radhakrishnan, Regunathan; Xiong, Ziyou; Memon, Nasir.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

*Proceedings of SPIE - The International Society for Optical Engineering.*vol. 5020, pp. 644-652, Security and Watermarking of Multimedia Contents V, Santa Clara, CA, United States, 1/21/03. https://doi.org/10.1117/12.477340

}

TY - GEN

T1 - On the Security of the Visual Hash Function

AU - Radhakrishnan, Regunathan

AU - Xiong, Ziyou

AU - Memon, Nasir

PY - 2003

Y1 - 2003

N2 - Robust hash functions are central to the security of multimedia content authentication systems. Such functions are sensitive to a key but robust to many allowed signal processing operations on the underlying content. 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 in this paper that the specific clustering arrived at by a robust hash function may be possible to learn. Specifically, we look at a well known robust hash function for image data called the Visual Hash Function (VHF). Given just an input and its hash value, 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 robust to many allowed signal processing operations on the underlying content. 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 in this paper that the specific clustering arrived at by a robust hash function may be possible to learn. Specifically, we look at a well known robust hash function for image data called the Visual Hash Function (VHF). Given just an input and its hash value, 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.

KW - Boosting

KW - Hash function

KW - Multimedia Content Authentication

KW - Visual Hash

KW - Weak Collision Property

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U2 - 10.1117/12.477340

DO - 10.1117/12.477340

M3 - Conference contribution

AN - SCOPUS:0242576290

VL - 5020

SP - 644

EP - 652

BT - Proceedings of SPIE - The International Society for Optical Engineering

A2 - Delp III, E.J.

A2 - Wah Wong, P.

ER -