The bit independence criterion was proposed to evaluate the security of the S-boxes used in block ciphers. This paper proposes an algorithm that extends this criterion to evaluate the degree of independence between the bits of inputs and outputs of the stream ciphers. The effectiveness of the algorithm is experimentally confirmed in two scenarios: random outputs independent of the input, in which it does not detect dependence, and in the RC4 ciphers, where it detects significant dependencies related to some known weaknesses. The complexity of the algorithm is estimated based on the number of inputs l, and the dimensions, n and m, of the inputs and outputs, respectively.

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PassPoint is a graphical authentication technique that is based on the selection of five points in an image. A detected vulnerability lies in the possible existence of a pattern in the points that make up the password. The objective of this work is to detect nonrandom graphical passwords in the PassPoint scenario. A spatial randomness test based on the average of Delaunay triangles’ perimeter is proposed, given the ineffectiveness of the classic tests in this scenario, which only consists of five points. A state-of-the-art of various applications of Voronoi polygons and Delaunay triangulations are presented to detect clustered and regular patterns. The distributions of the averages of the triangles’ perimeters in the PassPoint scenario for various sizes of images are disclosed, which were unknown. The test’s decision criterion was constructed from one of the best distributions to which the data were adjusted. Type I and type II errors were estimated, and it was concluded that the proposed test could detect clustered and regular graphical passwords in PassPoint, therefore being more effective in detecting clustering than regularity.

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