# 5D Gauss Map Perspective to Image Encryption with Transfer Learning Validation

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## Abstract

**:**

## 1. Introduction

## 2. Background and Index Terms

#### 2.1. One Dimensional Gauss Map

#### 2.2. Classification of Encrypted Images

#### 2.3. Transfer Learning vs. Machine Learning

- It allows you to train systems with less labeled data by reusing popular models that have previously been trained on massive datasets, making transfer learning a popular approach;
- It has the potential to cut down on training time and computer resources. The weights are not learned from starting with transfer learning since the pre-trained model has previously learned them based on earlier learning;
- You can use model topologies produced by the deep learning scientific community, such as AlexNet, GoogLeNet, and ResNet, which are popular designs

- 1.
- Selecting a pre-trained model: It is simpler to function with pre-trained models since there are many of them accessible on multiple platforms such as Googlenet, Squeezenet, and others;
- 2.
- Replacement of final layers: The final layers of the chosen pre-trained model are changed to retrain the network and to categorize a fresh batch of images and classes. The final wholly linked layer is changed to get a similar number of nodes as the number of new classes and a new classification layer that will provide an output based on the probabilities estimated by the layer. The final wholly linked layer will describe the new number of network classes that will it learn after the layers have been modified. The classification layer will select outputs from the new output categories accessible after modifying the layers;
- 3.
- Freezing the weights (Optional): By limiting the learning rates in such layers to zero, the weights of earlier layers in the network may be frozen. As a result, the characteristics of frozen layers are not modified throughout training; this significantly accelerates network training. In addition, freezing weights can help the network prevent overfitting if the new data set is tiny;
- 4.
- Retraining the model. The network will be retrained to understand and recognize the attributes associated with the new data and categories. Retraining usually needs less data than training a model from the start;
- 5.
- Predicting and assessing network accuracy. For example, one may classify fresh images and evaluate how well the network works once the model has been retrained [22].

#### 2.4. Deep Network Designing with MATLAB

- Time Series Forecasting;
- Classification of Sequences;
- Regression from one sequence to the next;
- Classification of Text Data;
- Classification of Image Data;
- Semantic Segmentation of Multispectral Images;
- Speech Recognition;
- Image Deblocking;
- Removing noise from a Color Image Using Pretrained models, and so on.

#### 2.5. Transfer Learning Using AlexNet

## 3. Proposed Method

#### 3.1. 5D Gauss Map Equation and Plot (Iteration)

#### 3.2. Algorithm for Image Encryption

#### 3.2.1. 5D Gauss Generator

#### 3.2.2. Permutation

- Random numbers are selected to achieve pixel permutation, P, Q, and R;
- With the help of these numbers, five sequences or indexes A, B, C, D, and E are generated;
- A pixel is shuffled in a row with sequence A and in a column with sequence B to create confusion;
- Here two times shuffling is done with row and column, the first time with A, B sequence and the second time with C and D sequence;
- The XOR process is the final stage in this encryption procedure. When using the XOR technique, the pixel intensities are changed to a new one, which cannot be inverted unless having the chaos key.

- Step 1: Read plaintext image I (the image size is set to $256\times 256$ pixels);
- Step 3: Using random numbers and chaotic sequences, generate index sequences A, B, C, D, and E;
- Step 4: To produce a confusion matrix, bring the index sequence A from Step 1 and shuffle the pixels in rows and columns with sequence B;
- Step 5: Pixels are shuffled in the row with sequence C and in the column with sequence D once more, resulting in scrambled image $IS$;
- Step 6: The index sequence E is then XORed with the scrambled image $IS$, resulting in the encrypted image.

## 4. Results and Facts

#### 4.1. Information Entropy

#### 4.2. Correlation Coefficient (CC)

#### 4.3. PSNR, MSE, and SSIM

#### 4.4. Differential Attack

#### 4.5. Keyspace

#### 4.6. Noise and Cropping Attack

#### 4.7. NIST Test

## 5. Validation of Classification Accuracy

## 6. Conclusions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Conflicts of Interest

## Abbreviations

5D | Five Dimensional |

1D | One Dimensional |

AES | Advanced Encryption Standard |

ANN | Artificial Neural Network |

CC | Correlation Coefficients |

CNN | Convolutional Neural Network |

D | Diagonal |

DES | Data Encryption Standard |

H | Horizontal |

HD | High Dimensional |

MSE | Mean Square Error |

PSNR | Peak Signal to Noise Ratio |

SAE | Sparse Auto Encoder |

SSIM | Structural Similarity Index Measure |

V | Vertical |

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**Figure 9.**Correlation coefficients of original images, (

**a**) Barbara, (

**b**) Baboon, (

**c**) Cameraman, (

**d**) Pepper, and (

**e**) Lena.

Images | PSNR | SSIM | MSE | Entropy Plain Image | Entropy Encrypted Image |
---|---|---|---|---|---|

Lena | ∞ | 1 | 0 | 7.5694 | 7.9621 |

Man | ∞ | 1 | 0 | 7.0097 | 7.8529 |

Peppers | ∞ | 1 | 0 | 7.5487 | 7.9533 |

Barbara | ∞ | 1 | 0 | 7.3410 | 7.9043 |

Baboon | ∞ | 1 | 0 | 7.3705 | 7.9578 |

Boat | ∞ | 1 | 0 | 7.1894 | 7.8794 |

Images | NPCR (%) | UACI (%) |
---|---|---|

Lena | 99.62 | 33.42 |

Man | 99.59 | 33.35 |

Peppers | 99.58 | 33.34 |

Barbara | 99.57 | 33.33 |

Baboon | 99.61 | 33.41 |

Boat | 99.64 | 33.43 |

**Table 3.**Correlation Coefficient (CC) Horizontal (H), Vertical (V), and Diagonal (D) values for the input test images.

Images | Original | Encrypted | ||||
---|---|---|---|---|---|---|

CC (H) | CC (V) | CC (D) | CC (H) | CC (V) | CC (D) | |

Lena | 0.9502 | 0.9712 | 0.9282 | 0.1100 | −0.0664 | −0.0752 |

Man | 0.9416 | 0.9630 | 0.9136 | 0.0843 | −0.0591 | −0.0682 |

Peppers | 0.9682 | 0.9725 | 0.9425 | 0.0007 | 0.0155 | −0.0042 |

Barbara | 0.8262 | 0.8942 | 0.8501 | 0.0061 | −0.0159 | −0.0123 |

Baboon | 0.6940 | 0.6039 | 0.6027 | 0.0391 | 0.0131 | −0.0080 |

Boat | 0.8864 | 0.9204 | 0.8400 | 0.1569 | −0.1549 | −0.1117 |

Images | Horizontal | Vertical | Diagonal | Entropy |
---|---|---|---|---|

Proposed | 0.1100 | −0.0664 | −0.0752 | 7.9621 |

[26] | −0.0414 | −0.0342 | 0.1083 | 7.4077 |

[27] | 0.0039 | 0.0059 | −0.0050 | 7.9994 |

[28] | 0.0008 | 0.0004 | 0.0020 | 7.9995 |

Images | PSNR of the Cropped Encrypted Image | PSNR of the Noisy Encrypted Image |
---|---|---|

Lena | 16.707916 | 32.354713 |

Man | 17.296258 | 31.669253 |

Peppers | 17.900450 | 32.064819 |

Barbara | 16.708817 | 31.032769 |

Baboon | 18.526831 | 32.952549 |

Boat | 18.855763 | 33.389143 |

Test | Values | Results |
---|---|---|

Frequency | 0.8315 | Pass |

Block Frequency | 0.2888 | Pass |

Cumulative Sums Forward | 0.5120 | Pass |

Cumulative Sums Reverse | 1.0000 | Pass |

Runs | 0.6572 | Pass |

Longest Run | 0.1339 | Pass |

Rank | 0.1885 | Pass |

FFT | 0.6250 | Pass |

Overlapping Template | 0.3525 | Pass |

Approximate Entropy | 0.9875 | Pass |

Linear Complexity | 0.1045 | Pass |

Serial | 0.1514 | Pass |

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**MDPI and ACS Style**

Salunke, S.; Ahuja, B.; Hashmi, M.F.; Marriboyina, V.; Bokde, N.D.
5D Gauss Map Perspective to Image Encryption with Transfer Learning Validation. *Appl. Sci.* **2022**, *12*, 5321.
https://doi.org/10.3390/app12115321

**AMA Style**

Salunke S, Ahuja B, Hashmi MF, Marriboyina V, Bokde ND.
5D Gauss Map Perspective to Image Encryption with Transfer Learning Validation. *Applied Sciences*. 2022; 12(11):5321.
https://doi.org/10.3390/app12115321

**Chicago/Turabian Style**

Salunke, Sharad, Bharti Ahuja, Mohammad Farukh Hashmi, Venkatadri Marriboyina, and Neeraj Dhanraj Bokde.
2022. "5D Gauss Map Perspective to Image Encryption with Transfer Learning Validation" *Applied Sciences* 12, no. 11: 5321.
https://doi.org/10.3390/app12115321