A method and system for cracking encrypted images based on deep learning
An encrypted image and deep learning technology, which is applied in the field of encrypted image cracking methods and systems based on deep learning, can solve problems such as the inability to find the correct key, and achieve the effect of solving difficult cracking and cracking takes a long time and a short time
- Summary
- Abstract
- Description
- Claims
- Application Information
AI Technical Summary
Problems solved by technology
Method used
Image
Examples
Embodiment 1
[0068] The encrypted image cracking method based on deep learning provided by the embodiment of the present invention includes the following steps:
[0069] Step 1: Obtain encrypted image samples.
[0070] Among them, the original image can be obtained from various open source datasets, such as Mnist and Celeba datasets. After obtaining the original image, write the code to encrypt the data set according to encryption algorithms such as Arnoldcat and AES, and then the encrypted image sample can be obtained.
[0071] Step 2: Construct a deep learning network model on the basis of the Gan generation confrontation network.
[0072] Specifically, the deep learning network model is constructed according to the Gan generation confrontation network, including the generation network and the confrontation network. The generation network needs to restore the encrypted image with the original image as the target, and the confrontation network is a discriminator to judge the Whether the...
Embodiment 2
[0080] The encrypted image cracking method based on deep learning provided by the embodiment of the present invention specifically includes the following steps:
[0081] Step 1: Obtain encrypted image samples.
[0082] Among them, the original image can be obtained from various open source datasets, such as Mnist and Celeba datasets. After obtaining the original image, write the code to encrypt the data set according to encryption algorithms such as Arnoldcat and AES, and then the encrypted image sample can be obtained.
[0083] Step 2: Construct a deep learning network model based on the Autoencoder autoencoder and the Gan generation confrontation network.
[0084] Specifically, the deep learning network model is constructed based on the Autoencoder self-encoder and the Gan generation confrontation network, including the generation network and the confrontation network. Among them, the generation network needs to restore the encrypted image with the original image as the tar...
PUM
Login to View More Abstract
Description
Claims
Application Information
Login to View More 

