This paper presents a new watermarking framework, suitable for authentication of H.264 compressed videos. The authentication data is embedded as fragile, blind and erasable watermark with low video quality degradations. Because of using a fragile watermark, hard authentication is possible. In contrast to other approaches, the watermarking is done after the H.264 compression process. Hence, the authentication information can be embedded in already encoded videos. To reconstruct the original H.264 compressed video the watermark can be removed. The framework is based on a new transcoder, which analyses the original H.264 bit stream, computes a watermark, embeds the watermark and generates a new H.264 bit stream. To authenticate the video a hash value is used. This value is encrypted with a private key of an asymmetric cryptosystem. The payload of the watermark consists of the encrypted hash value and a certificate with the public key. Some skipped macroblock of the H.264 video are used to embed the watermark. A special process selects these macroblocks. This process sets the distribution and the number of skipped blocks as well as the number of embedded bits per block to achieve low video quality degradations and low data rate. To embed the watermark the performance of several approaches is discussed and analyzed. The result of the framework is a new watermarked H.264 bit stream. All data necessary for authentication are embedded and cannot get lost.
In nontarget screening, structure elucidation of small molecules from high resolution mass spectrometry (HRMS) data is challenging, particularly the selection of the most likely candidate structure among the many retrieved from compound databases. Several fragmentation and retention prediction methods have been developed to improve this candidate selection. In order to evaluate their performance, we compared two in silico fragmenters (MetFrag and CFM-ID) and two retention time prediction models (based on the chromatographic hydrophobicity index (CHI) and on log D). A set of 78 known organic micropollutants was analyzed by liquid chromatography coupled to a LTQ Orbitrap HRMS with electrospray ionization (ESI) in positive and negative mode using two fragmentation techniques with different collision energies. Both fragmenters (MetFrag and CFM-ID) performed well for most compounds, with average ranking the correct candidate structure within the top 25% and 22 to 37% for ESI+ and ESI- mode, respectively. The rank of the correct candidate structure slightly improved when MetFrag and CFM-ID were combined. For unknown compounds detected in both ESI+ and ESI-, generally positive mode mass spectra were better for further structure elucidation. Both retention prediction models performed reasonably well for more hydrophobic compounds but not for early eluting hydrophilic substances. The log D prediction showed a better accuracy than the CHI model. Although the two fragmentation prediction methods are more diagnostic and sensitive for candidate selection, the inclusion of retention prediction by calculating a consensus score with optimized weighting can improve the ranking of correct candidates as compared to the individual methods. Graphical abstract Consensus workflow for combining fragmentation and retention prediction in LC-HRMS-based micropollutant identification.
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