In order to solve the problem that the currently widely used 3D morphable model has insufficient expression ability, resulting in poor generalization performance of the reconstructed 3D face model, a novel method for 3D face reconstruction and dense face alignment based on a single face image under unknown pose, expression and illumination was proposed. First, the existing 3D morphable model was improved by convolutional neural network to improve the expression ability of the 3D face model. Then, based on the smoothness of the face and the similarity of the image, a new loss function was proposed at the feature point and pixel level, and the weakly-supervised learning was used to train the convolutional neural network model. Finally, the trained network model was used to perform the 3D face reconstruction and dense face alignment. Experimental results show that, for 3D face reconstruction, the proposed model has the normalized mean error on AFLW2000-3D reduced to 2.25, and for dense face alignment, the proposed model has the normalized mean errors on AFLW2000-3D and AFLW-LFPA reduced to 3.80 and 3.34 respectively. Compared with the original method using 3D morphable model, the proposed model has the normalized mean errors reduced by 7.4% and 7.8% respectively in 3D face reconstruction and dense face alignment. Therefore, for face images with different lighting environments and angles, this network model is accurate in reconstruction and robust, and has high 3D face reconstruction and dense face alignment quality.

Aiming at the simple obfuscating result of the existing control flow obfuscating algorithm, an obfuscating algorithm based on congruence equation and improved flat control flow was presented. First of all, a kind of opaque predicate used in the basic block of the source code was created by using secret keys and a group of congruence equation. Then, a new algorithm for creating N-state opaque predicate was presented based on Logistic chaotic mapping. The proposed algorithm was applied to the existing flat control flow algorithm for improving it. Finally, according to the combination of the above two proposed algorithms for obfuscating the source code, the complexity of the flat control flow in the code was increased and make it more difficult to be cracked. Compared with the flat control flow algorithm based on chaotic opaque predicate, the code's tamper-proof attack time of the obfuscated code was increased by above 22% on average and its code's total cyclomatic complexity was improved by above 34% on average by using the proposed obfuscating algorithm. The experimental results show that, the proposed algorithm can guarantee the correctness of execution result of the obfuscated program and has a high cyclomatic complexity, so it can effectively resist static and dynamic attacks.