Sensor pattern noise (SPN) has been recognized as a reliable device fingerprint for camera source identification (CSI) and image origin verification. However, the SPN extracted from a single image can be contaminated largely by image content details from scene because, for example, an image edge can be much stronger than SPN and hard to be separated. So, the identification performance is heavily dependent upon the purity of the estimated SPN. In this paper, we propose an effective SPN predictor based on eight-neighbor context-adaptive interpolation algorithm to suppress the effect of image scene and propose a source camera identification method with it to enhance the receiver operating characteristic (ROC) performance of CSI. Experimental results on different image databases and on different sizes of images show that our proposed method has the best ROC performance among all of the existing CSI schemes, as well as the best performance in resisting mild JPEG compression, especially when the false-positive rate is held low. Because trustworthy CSI must often be performed at low false-positive rates, these results demonstrate that our proposed technique is better suited for use in real-world scenarios than existing techniques. However, our proposed method needs many such as not less than 100 original images to create camera fingerprint; the advantage of the proposed method decreases when the camera fingerprint is created with less original images.

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The fermentation production of platform chemicals in biorefineries is a sustainable alternative to the current petroleum refining process. The natural advantages of Corynebacterium glutamicum in carbon metabolism have led to C. glutamicum being used as a microbial cell factory that can use various biomass to produce value-added platform chemicals and polymers. In this review, we discussed the use of C. glutamicum surface display engineering bacteria in the three generations of biorefinery resources, and analyzed the C. glutamicum engineering display system in degradation, transport, and metabolic network reconstruction models. These engineering modifications show that the C. glutamicum engineering display system has great potential to become a cell refining factory based on sustainable biomass, and further optimizes the inherent properties of C. glutamicum as a whole-cell biocatalyst. This review will also provide a reference for the direction of future engineering transformation.

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The display of recombinant proteins on the surfaces of bacteria is a research topic with many possible biotechnology applications—among which, the choice of host cell and anchoring motif is the key for efficient display. Corynebacterium glutamicum is a promising host for surface display due to its natural advantages, while single screening methods and fewer anchor proteins restrict its application. In this study, the subcellular localization (SCL) predictor LocateP and tied-mixture hidden Markov models were used to analyze all five known endogenous anchor proteins of C. glutamicum and test the accuracy of the predictions. Using these two tools, the SCLs of all proteins encoded by the genome of C. glutamicum 13032 were predicted, and 14 potential anchor proteins were screened. Compared with the positive controls NCgl1221 and NCgl1337, three anchoring proteins—NCgl1307, NCgl2775, and NCgl0717—performed better. This study also discussed the applicability of the anchor protein screening method used in this experiment to other bacteria.