Image authentication using LBP-based perceptual image hashing

Feature extraction is a main step in all perceptual image hashing schemes in which robust features will led to better results in perceptual robustness. Simplicity, discriminative power, computational efficiency and robustness to illumination changes are counted as distinguished properties of Local Binary Pattern features. In this paper, we investigate the use of local binary patterns for perceptual image hashing. In feature extraction, we propose to use both sign and magnitude information of local differences. So, the algorithm utilizes a combination of gradient-based and LBP-based descriptors for feature extraction. To provide security needs, two secret keys are incorporated in feature extraction and hash generation steps. Performance of the proposed hashing method is evaluated with an important application in perceptual image hashing scheme: image authentication. Experiments are conducted to show that the present method has acceptable robustness against perceptual content-preserving manipulations. Moreover, the proposed method has this capability to localize the tampering area, which is not possible in all hashing schemes

Perceptual hashing-based movement compensation applied to in vivo two-photon microscopy

Le mouvement animal, présent lors d’expériences in vivo effectuées à l’aide de microscopie à effet deux photons, nuit à l’observation de phénomènes biologiques et à l’analyse subséquente des flux vidéos acquis. Ceci s’explique entre autres par le fait que, dû au sectionnement optique, tout déplacement dans l’axe z (perpendiculaire au plan d’imagerie) modifie drastiquement l’image et ne permet qu’une observation instable de l’échantillon examiné. En appliquant une fonction de hachage aux images acquises, nous produisons des vecteurs décrivant les qualités perceptuelles de ces images ; ces vecteurs peuvent alors servir à comparer les images une à une, en temps réel. Ces comparaisons nous permettent de réunir les images en groupes correspondant à des plans z distincts. Ainsi, du processus de hachage, de comparaison et de groupage d’images résulte une méthode logicielle de compensation de mouvement en temps réel qui peut être utilisée dans le cadre d’expériences biologiques en laboratoire.Animal movement during in vivo two-photon microscopy experiments hinders efforts at observing biological phenomena and the subsequent analysis of the acquired video streams. One of the reasons for this is that, due to optical sectioning, any displacement in the z-axis (perpendicular to the plane of imaging) dramatically changes the collected image and thus provides the experimenter with an unstable view of the imaged sample. By applying a hashing function on the acquired video frames, we produce vectors embodying the images’ perceptual qualities; these vectors can then be used to compare the frames one to another, in real-time. These comparisons allow us to group similar images in clusters corresponding to distinct z-planes. In effect, the process of perceptually hashing, comparing and grouping video frames provides us with software-based, real-time movement compensation which can be used in a biological laboratory setting

Actas de las XXXIV Jornadas de Automática

Postprint (published version