Robust video fingerprinting based on symmetric pairwise boosting
IEEE Transactions on Circuits and Systems for Video Technology
ICME'09 Proceedings of the 2009 IEEE international conference on Multimedia and Expo
A robust image identification using trace-hausdorff combination
ISCIT'09 Proceedings of the 9th international conference on Communications and information technologies
A framework for video forensics based on local and temporal fingerprints
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Proceedings of the international conference on Multimedia
Lexicographical framework for image hashing with implementation based on DCT and NMF
Multimedia Tools and Applications
Structural Feature-Based Image Hashing and Similarity Metric for Tampering Detection
Fundamenta Informaticae
Random Gray code and its performance analysis for image hashing
Signal Processing
Video fingerprinting using Latent Dirichlet Allocation and facial images
Pattern Recognition
Watermarking is not cryptography
IWDW'06 Proceedings of the 5th international conference on Digital Watermarking
Robust 3D mesh model hashing based on feature object
Digital Signal Processing
Robust image hashing using non-uniform sampling in discrete Fourier domain
Digital Signal Processing
Fast communication: Robust image hashing using ring-based entropies
Signal Processing
Key-dependent 3D model hashing for authentication using heat kernel signature
Digital Signal Processing
Content-based copy detection through multimodal feature representation and temporal pyramid matching
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
| Hi-index | 0.00 |
Robust signal hashing defines a feature vector that characterizes the signal, independently of "nonsignificant" distortions of its content. When dealing with images, the considered distortions are typically due to compression or small geometrical manipulations. In other words, robustness means that images that are visually indistinguishable should produce equal or similar hash values. To discriminate image contents, a hash function should produce distinct outputs for different images. Our paper first proposes a robust hashing algorithm for still images. It is based on radial projection of the image pixels and is denoted the Radial hASHing (RASH) algorithm. Experimental results provided on the USC-SIPI dataset reveal that the proposed RASH feature vector is more robust and provides much stronger discrimination than a conventional histogram-based feature vector. The RASH vector appears to be a good candidate to build indexing algorithms, copy-detection systems, or content-based authentication mechanisms. To take benefit from the RASH vector capabilities, video content is summarized into key frames, each of them characterizing a video shot and described by its RASH vector. The resulting video hashing system works in real time and supports most distortions due to common spatial and temporal video distortions.