Novel large scale digital forensics service platform for internet videos

NOVEL LARGE SCALE DIGITAL FORENSICS SERVICE PLATFORM FOR INTERNET VIDEOS

Abd El-Fattah Hussein Mahran

AGENDA Problem Definition Problem solution Content delivery network Proposed Architecture overview Challenges Main paper contribution Digital Video Forensics (DVF) Techniques Digital Video Forensics Systems Proposed System Architecture System Deployment and Performance Evaluation Conclusion Future work References

PROBLEM DEFINITION

Increasing of Transmission of illegal videos over the Internet

PROBLEM SOLUTION

Developing of large scale digital video forensics system for prosecuting & deterring digital crimes in the Internet.

Paper gives a proposal, design & implementation of a novel large scale digital forensics service platform (DFSP) that can effectively detect illegal content from the Internet Videos.

WHAT IS CDN A content delivery network (CDN) is a large distributed

system of servers deployed in multiple data centers in the Internet.

The goal of a CDN is to serve content to end-users with high availability and high performance. CDNs serve a large fraction of the Internet content today, including web objects (text, graphics, URLs and scripts), downloadable objects (media files, software, documents), applications (e-commerce, portals), live streaming media, on-demand streaming media, and social networks.

Several protocol suites are designed to provide access to a wide variety of content services distributed throughout a content network. The Internet Content Adaptation Protocol (ICAP) , A more recently defined and robust solution is provided by the Open Pluggable Edge Services (OPES) protocol.

PROPOSED ARCHITECTURE OVERVIEW A distributed architecture taking the advantage of

Content Delivery Network (CDN) to improve scalability which gives the advantage to process enormous number of Internet Videos in real time.

Proposed architecture will be specifically a CDN based Resource Aware scheduling (CRAS) algorithm which schedules the tasks efficiently in the DFSP according to the resource parameters such as delay & computation load.

The DFSP will be deployed in the Internet which integrates the CDN based distributed architecture & CRAS algorithm with a large scale video detection algorithm & evaluate the deployed system.

Evaluation results demonstrates the effectiveness of the Platform.

TODAY Number of the Digital Videos distributed over the Internet increases

exponentially which creates a need to solve their security problems. Studies identified that 23.8% of the global internet traffic can be associated

with the illegal distribution of copyrighted work & 91% of the Internet pornographic materials are videos that could be downloaded from different media sources.

Reacting to all of this raises the needs to develop a digital forensics system for large scale Internet videos

Most existing Forensics Systems focus on how to deal with content robustness & identification of their accuracy leaving the consideration of detecting the legality of the large scale Internet videos in real time manner even those reports which works on the efficiency & scalability for large scale video content identification focus on solving these problems from video focus on solving these problems from video

retrieval algorithm perspectiveretrieval algorithm perspective

Through this paper, a different dimension will be addressed which is the efficiency & scalability issues from system perspectiveissues from system perspective

CHALLENGES

The large amount of computation brought by analyzing & detecting large volume of video data. This makes it difficult to serve a large number of users concurrently.

The large amount of communication brought by transmitting a great volume of video data from media sources to the system.

SCALABILITY PROBLEM

To solve the Scalability problem, paper propose to build DFSP upon CDN pushing the massive forensics tasks to the most appropriate CDN nodes thereby effectively reducing the computational cost & communication cost.

EFFICIENCY PROBLEM To solve efficiency problem , paper proposes CDN-based

Resource-Aware Scheduling (CRAS) algorithm.

Existing network-aware resource scheduling algorithms include non-adaptive scheduling algorithms that use some heuristics to select nodes, and adaptive scheduling algorithms that take the current network or server conditions into account.

In DFSP, with CRAS algorithm, user requests can be directed to appropriate nodes. Different forensics tasks are assigned to different CDN nodes based on not only the network conditions but also the computation load, thereby the massive data stream can be in parallel scheduled among multiple nodes efficiently also it is proposed to integrate the proposed CDN-based distributed architecture and CRAS algorithm with a Large-scale Video Detection (LVD) algorithm.

MAIN PAPER CONTRIBUTION Proposing & design of a novel large-scale

digital video forensics service architecture and platform, which can process enormous number of Internet videos in real time by employing CDN.

Proposing a CDN-based Resource-Aware Scheduling algorithm for dynamic load balancing in DFSP, which can significantly improve the efficiency of the platform.

Implementing & evaluating a deployed system in large scale, which integrates the CDN-based distributed architecture & the CDN-based resource-aware scheduling algorithm with a large-scale video detection algorithm.

DIGITAL VIDEO FORENSICS (DVF) TECHNIQUES

I) WatermarkingI) Watermarking is a traditional forensics technique for detecting illegal copies & digital tampering which has improvement aspects:Watermark must be embedded in legal videos prior to video distributionNature of the original data will be changedWatermark could be attacked or destroyed during transmission.

DIGITAL VIDEO FORENSICS (DVF) TECHNIQUES

II) Video fingerprintII) Video fingerprint technique has drawn wide attention, this technique can identify illegal content by extracting a unique fingerprint from video data.

The fingerprint can be extracted based on some static features such as color, texture and shape, or some motion features of the video.

The major advantage of fingerprint technique is that the fingerprint can be extracted after the media has been distributed and will not change the original data; thus, it is a very useful method for detecting Internet videos.

DIGITAL VIDEO FORENSICS SYSTEMS Douze presented a video copy detection system, which

used a precise representation method to decide whether or not a query video segment was a copy of a video from the indexed dataset.

Xu explored an effective system for analyzing the high-level structures and extracting useful features from soccer videos in order to identify the content of videos.

Gauch and Shivadas presented a commercial identification system by extracting features from video sequences that can characterize the temporal and chromatic variations within each clip.

Shen outlined a system for detecting near-duplicate videos based on the dominating content and content changing trends of the videos .

In addition to the above systems, some other similar ones also provided effective methods for video forensics and achieved good results. However, all this work analyzes and processes videos on a single server.

IMPROVING FORENSICS EFFICIENCYTo improve the forensic efficiency, some researchers began to study the data structure for effectively indexing the video features. However, the approaches are all based on single server.

For example, Hoad and Zobel , Hoad and Zobel used local alignment to find sequences of similar values in video and clip, which provided much faster searching .

LejsekLejsek achieved good efficiency greatly depending on a specific hardware configuration, which is outside the range of usual computer workstations ;thus, their work may not be practical for large-scale deployments.

ZhaoZhao improved the scalability of several well-known features including color signature and visual keywords for web-based retrieval by using high-dimensional indexing techniques .

Recently, ShangShang introduced a compact spatiotemporal feature to represent videos and constructed an efficient data structure to address the efficiency and scalability issues for real-time large-scale near-duplicate Web video retrieval.

Although these works have made good use of the server capacity, Although these works have made good use of the server capacity, when the number of videos continues to scale up, the real-time when the number of videos continues to scale up, the real-time

performance may be still hard to be guaranteed.performance may be still hard to be guaranteed.

PROPOSED SYSTEM ARCHITECTUREIt consists of three main components:

1. Content Access (CA)2. Video Detection (VD)3. Resource Management (RM)

Content Access (CA):Content Access (CA): It is located on each CDN node ; which is used to obtain video data from certain media sources. During this process, we use web crawler, a special technology for web search, to collect data. This technology can methodically scan or “crawl” through Internet pages to create an index of video data, so that CA can quickly provide the relevant data to detect and regularly ensure the data are up to date.



Video Detection (VD): Video Detection (VD): VD is a group of servers distributed near CDN nodes, which are responsible for analyzing video content and judging their legality. It is composed of “Blacklist” Database, Content Analysis Servers, and Searching and Matching Servers.

“Blacklist” Database stores a copy of fingerprints of improper videos. Content Analysis Servers are used to analyze the video content and

extract their fingerprints. Searching and Matching Servers take charge of comparing these

fingerprints with those pre-stored in “Blacklist” Database, and judging their legality.

Paper proposes a large-scale video detection algorithm in VD



Resource Management (RM): Resource Management (RM): RM controls and monitors the whole platform, in charge of scheduling, coordinating, and managing all the resources and tasks. It comprises Network Monitoring module and Load Balancing module.

Since each node has a copy of “blacklist”, the forensics tasks can be done at any node. Network Monitoring module is in charge of monitoring all the nodes and media sources, so that Load Balancing module can coordinate each node and schedule different tasks depending on the overall situation.

To balance the load among multiple nodes, paper propose CDN-based Resource-Aware Scheduling algorithm.



LARGE-SCALE VIDEO DETECTION LARGE-SCALE VIDEO DETECTION

To identify illegal content in DFSP, paper propose a large-scale video detection algorithm, which represents video by spatial-temporal spatial-temporal video wordsvideo words and computes the relevance score by language modeling language modeling approachapproach

PROPOSED SYSTEM ARCHITECTURE

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SYSTEM DEPLOYMENT AND PERFORMANCE EVALUATION

System Deployment DFSP is deployed on ChinaCache, the biggest

CDN provider in China. Three Central “Blacklist” Database are deployed to back up each other. Around these central databases, 55 CDN nodes (about 500 servers) are located within each district in China.

DETECTION ACCURACY EVALUATION

Color Histogram-based methodLocal Feature-based methodLSH-based method

EFFICIENCY EVALUATION

SCALABILITY EVALUATION DFSP has a relatively stable

detection time with the increasing number of user queries, which demonstrates better scalability. The average detection time is 35.2 s.

DFSP Despite the heavy demands on the video detection, the DFSP architecture is able to distribute the computation load among several nodes, which saves computation time significantly.

CONCLUSION

Proposed, designed, and implemented a novel large-scale digital forensics service platform for Internet videos.

Proposed DFSP architecture is built upon CDN infrastructure

Proposed CRAS algorithm to solve dynamic load balancing problems in large scale, thereby achieving high system efficiency

Deployed DFSP on ChinaCache and performed evaluation. The evaluation results demonstrate the effectiveness of the DFSP.

FUTURE WORK

Reduce the storage and computation complexity for processing a great amount of video data

REFERENCES

Novel Large Scale Digital Forensics Service Platform for Internet Videos

http://en.wikipedia.org/wiki/HSL_and_HSV http://en.wikipedia.org/wiki/Information_retrie

val#Recall

QUESTIONS

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Technology

Novel large scale digital forensics service platform for internet videos