This paper explores the various techniques used to authenticate the visual data recorded by the automatic video surveillance system. Automatic video surveillance systems are used for continuous and effective monitoring and reliable control of remote and dangerous sites. Some practical issues must be taken in to account, in order to take full advantage of the potentiality of VS system.
Description of Image Authentication Techniques
The validity of visual data acquired, processed and possibly stored by the VS system, as a proof in front of a court of law is one of such issues. But visual data can be modified using sophisticated processing tools without leaving any visible trace of the modification. So digital or image data have no value as legal proof, since doubt would always exist that they had been intentionally tampered with to incriminate or exculpate the defendant.
Besides, the video data can be created artificially by computerized techniques such as morphing. Therefore the true origin of the data must be indicated to use them as legal proof. By data authentication we mean here a procedure capable of ensuring that data have not been tampered with and of indicating their true origin.
Automatic Visual Surveillance System
Automatic Visual Surveillance system is a self monitoring system which consists of a video camera unit, central unit and transmission networks A pool of digital cameras is in charge of frame the scene of interest and sent corresponding video sequence to central unit. The central unit is in charge of analyzing the sequence and generating an alarm whenever a suspicious situation is detected.
Central unit also transmits the video sequences to an intervention centre such as security service provider, the police department or a security guard unit. Somewhere in the system the video sequence or some part of it may be stored and when needed the stored sequence can be used as a proof in front of court of law.
If the stored digital video sequences have to be legally credible, some means must be envisaged to detect content tampering and reliably trace back to the data origin.