The invention relates to a
ghost imaging neural
differential analysis method under a selected
plaintext attack condition in the technical field of computational imaging and
optical security. The method comprises the following steps: firstly, designing a differentiated
plaintext, and deducing a projection speckle by adopting differential calculation; then, constructing a
dimensionality reduction simulation training set and an experimental
test set based on the structural features of the projection speckles; then, constructing a one-dimensional
signal noise reduction neural
network model, and training the one-dimensional
signal noise reduction neural
network model by adopting the dimension reduction
simulation training set; and finally, inputting the experimental
test set into the trained one-dimensional
signal noise reduction neural
network model to obtain deciphered speckles, and based on the deciphered speckles, adopting
ghost imaging correlation operation to obtain a cracked
plaintext image. That is to say, based on the basic principle of
ghost imaging encryption, the inherent linear property of the ghost imaging light path is utilized, and the fusion architecture of the differential
attack and the neural network is used, so that the
cracking of various ghost imaging
encryption methods can be realized, and the universality and reliability of ghost imaging
encryption analysis can be improved.