# Image Watermarking between Conventional and Learning-Based Techniques: A Literature Review

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## Abstract

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## 1. Introduction

## 2. Image Watermarking Background

#### 2.1. Embedding Phase

#### 2.1.1. Additive Method

#### 2.1.2. Substitutive Method

#### 2.2. Detection Phase

#### 2.2.1. Blind Watermarking

#### 2.2.2. Non-Blind Watermarking

#### 2.3. Transmission Phase

#### 2.3.1. Unintentional Attacks

**Compression:**Compression attacks are designed to reduce the amount of data encoded in the host document. This process consists of removing the perceptibly less important components while preserving the important components. JPEG and JPEG2000 are considered the most popular image compression algorithms currently in use [42].**Filtering:**Generally, filtering is used for noise reduction. This type of filtering basically has the effect of attenuating the high-frequency components of the image and therefore degrading the brand components inserted in these frequencies.**Volumetric transformations:**The basic idea behind these transformations is to adjust the brightness value of each pixel in an image using a linear or non-linear function to improve the visual aspect of the image. These transformations include histogram spreading and equalization, Gamma transformation, and so on.**Noise:**The addition of noise increases the uncertainty or ambiguity of the average information embedded in the image, which can have a masking effect on the watermark and therefore disturb its detection/extraction.**Geometric transformations:**This kind of transformation does not remove the mark, rather, it makes it undetectable while still remaining in the image. In the majority of watermarking algorithms, the watermark detector requires knowledge of the exact position of the mark in the image. These transformations cause a desynchronization between the mark inserted in the image and the detector [43]. There are several geometric transformations, for example, affine geometrical transformations (translation, rotation, and change of scale), and cropping, which consists of removing a part of the image and consequently a part of the mark. There are also local geometric transformations, such as the StirMark attack. This attack consists of a succession of random geometric distortions applied locally to several places in the image [44].

#### 2.3.2. Intentional Attacks

**Cryptographic attack:**The main goal of this attack is to crack the security codes and algorithms used in watermarking methods in order to remove the inserted watermark information. In this case, the attacker tries to identify the embedded secret key using exhaustive search techniques. This type of attack possesses high computational costs and it is limited, and therefore it is employed less frequently.**Protocol attack:**The idea behind this attack is to create document ownership ambiguity. Some protocol attacks are based on invertible operations, in which an attacker subtracts their watermark from the watermarked document and claims its ownership. Another type of protocol attack is the copy attack [45]. In this case, the goal is to remove the watermark from one container and place it in another target document. In all cases, the attacker can claim for the ownership of both the original and watermarked image.

## 3. Performance Requirements in Digital Watermarking

#### 3.1. Robustness

**Robust watermarking:**In this case, the watermark must be resistant to different attacks on the digital document, and the detection of the watermark must be effective even under those manipulations [47]. This watermarking technique is suitable for a variety of applications, including copyright protection, fingerprinting, broadcast monitoring, and copy control [48].**Fragile watermarking:**In fragile watermarking, the mark is extremely sensitive to the modifications of the watermarked document [49,50]. This technique is used to prove the authenticity and integrity of multimedia data. A fragile watermarking technique is designed to detect (with a high probability) any kind of manipulation of the watermarked document, including both incidental and intentional attacks. A comparison of the extracted watermark and the original watermark is performed to identify if the document is modified or not.

**Normalized Correlation (NC):**NC computes the similarity and difference between the original watermark and the extracted watermark. The NC value of a good watermarking algorithm should be ≥0.7. Ideal algorithms provide an NC equal to 1. NC is defined as follows:$$NC=\frac{{\sum}_{i=1}^{X}{\sum}_{j=1}^{Y}({W}_{or{g}_{ij}}\times {W}_{ex{t}_{ij}})}{{\sum}_{i=1}^{X}{\sum}_{j=1}^{Y}{W}_{or{g}_{ij}^{2}}}$$**Bit Error Rate (BER):**BER is also one of the measurements that evaluate the robustness of a watermarking technique. BER computes the ratio value of incorrectly extracted watermark bits from the inserted watermark bits. The closer the BER value is to 0, the greater the similarity between the extracted watermark and the original one, indicating more robustness.$$BER=\frac{NI}{NT}$$NI is the number of incorrectly extracted bits, while NT is the total number of bits transmitted.

#### 3.2. Imperceptibility

**Peak signal-to-noise ratio (PSNR):**PSNR is the most commonly used indicator for providing quantitative scores across the watermarked images. PSNR evaluates the invisibility requirements by comparing the similarity of the original image file to the watermarked one. The PSNR is defined as:$$PSNR={log}_{10}\left(\frac{{d}^{2}}{MSE}\right)$$$$MSE=\frac{1}{M\times N}\sum _{x=0}^{M-1}\sum _{y=0}^{N-1}{({I}_{0}(x,y)-{I}_{w}(x,y))}^{2}$$**Structural similarity index model (SSIM):**The SSIM evaluates the structural similarity existing between two images. Unlike PSNR, which is based on the pixel-to-pixel difference between two images. The SSIM measure consists of combining three parameters: brightness, contrast, and structure comparison. It is computed on several windows of an image. The measure between two windows x and y is given by the following:$$SSIM(x,y)={\left[l(x,y)\right]}^{\alpha}.{\left[c(x,y)\right]}^{\beta}.{\left[s(x,y)\right]}^{\gamma}$$$$MSSIM({I}_{0},{I}_{w})=\frac{1}{m}\sum _{i=0}^{m}SSIM({x}_{i},{y}_{i})$$

#### 3.3. Capacity

#### 3.4. Complexity

#### 3.5. Security

**Number of Changing Pixel Rate (NPCR)**and**Unified Averaged Changed Intensity (UACI)**: Those two measures are used to evaluate the efficiency of image watermarking against potential attacks. They are usually used to analyze the resistance of the watermarked images to pixel-level changes. NPCR and UACI scores should always be close to 1 and 0.33, respectively, to achieve good security. Higher values mean resistance will be better. If W and $\overline{W}$ denote the original and the extracted watermark, respectively, NPCR and UACI are defined as:$$NPCR=\frac{{\sum}_{i=1}^{M}{\sum}_{j=1}^{N}D(i,j)}{M\times N}\times 100\phantom{\rule{0.166667em}{0ex}}\phantom{\rule{0.166667em}{0ex}}\phantom{\rule{0.166667em}{0ex}}\phantom{\rule{0.166667em}{0ex}}(\%)$$$$D(i,j)=\left\{\begin{array}{ccc}1\hfill & \phantom{\rule{1.em}{0ex}}& if\phantom{\rule{0.166667em}{0ex}}W(i,j)=\overline{W(i,j)}\hfill \\ 0\hfill & \phantom{\rule{1.em}{0ex}}& Otherwise\hfill \end{array}\right.$$$$UACI=\frac{{\sum}_{i=1}^{M}{\sum}_{j=1}^{N}|W(i,j)-\overline{W(i,j)}|}{M\times N\times 255}\times 100\phantom{\rule{0.166667em}{0ex}}\phantom{\rule{0.166667em}{0ex}}\phantom{\rule{0.166667em}{0ex}}\phantom{\rule{0.166667em}{0ex}}(\%)$$

#### 3.6. False Positive Rate (FPR)

## 4. Conventional Image Watermarking Schemes

#### 4.1. Spatial Domain

#### 4.2. Transform Domain

#### 4.2.1. Discrete Cosine Transform (DCT)

#### 4.2.2. Discrete Fourier Transform (DFT)

#### 4.2.3. Discrete Wavelet Transform (DWT)

#### 4.3. Hybrid Domain

## 5. Deep Learning-Based Image Watermarking Schemes

#### 5.1. Learning-Based Embedding Techniques

#### 5.2. Learning-Based Extraction Techniques

#### 5.3. End-to-End Learning-Based Watermarking Techniques

## 6. Discussion, Issues and Opportunities

## 7. Conclusions

## Author Contributions

## Funding

## Conflicts of Interest

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**Figure 9.**Brief diagram of the learning framework in the image watermarking process. The network was trained either to insert the watermark (orange line) or to detect the watermark (sky blue line). The scenario in which both embedding and detection are performed by a trained network is represented by the dark blue line, while input processing and output processing using learning networks are shown by the red line and the green line, respectively.

Reference | Approaches | Objectives | Cover/Watermark Size | Evaluation Metric | Advantages and Weaknesses |
---|---|---|---|---|---|

[70] | Invariant significant bitplane probability density | Robustness against desynchronization attacks | 512 × 512 pseudorandom sequence of 85 bits | PSNR, MSE, SSIM | + Resistance to common image-processing attacks and desynchronization attacks - High computational complexity - Less robust against JPEG compression and scaling |

[73] | LSB, ISB | Tamper detection and localization of medical/general images | 256 × 256 64 × 64 | PSNR, SSIM, BER | + Low complexity + Highly visual image - Fragile - Lack of irreversibility |

[74] | LSB substitution mechanism | Enhance the conventional LSB technique of digital image watermarking | 700 × 700 100 × 100 | PSNR, NCC | + Low complexity + High-quality image - Non-blind - Less robust against median filtering and speckle noise |

Reference | Approaches | Objectives | Cover/Watermark Size | Evaluation Metric | Advantages and Weaknesses |
---|---|---|---|---|---|

[83] | DCT DE KELM | Achieve good balance between robustness and invisibility | $512\times 512$ $32\times 32$ | PSNR, NC | + Robust against numerous attacks - Fragile to rotational attacks |

[85] | DCT JND Arnold transform | Achieve good balance between robustness and invisibility | 512 × 512 64 × 64 | PSNR, SSIM, NC, BER | + Resist different attacks, such as median filter and image rotation - Security and complexity analysis are inadequate |

[94] | QDFT EPA MSPSAM MM, PSO | Provide better robustness against JPEG attacks and contrast enhancement | $512\times 512$ $64\times 64$ | Objective Value PSNR MSSIM BER | + Resist several image-processing attacks, including JPEG attacks - High computational time |

[95] | DFT, PSO | Improve performance, in particular the security | COCO dataset 32-bit binary watermark | VIF PSNR Average BCR BER | + Withstand image-processing attacks and geometrical ones - Payload needs to be improved |

[101] | DWT Entropy Alpha blending | Reduce computational cost considering robustness and imperceptibility | $512\times 512$ $64\times 64$ | PSNR SSIM NCC | + Low computational cost + Higher PSNR value + Robust against various sorts of attacks - Non-blind - Testing with one type of watermark data is inadequate - Security and complexity analysis need to be discussed in detail |

[102] | DWT Paillier cryptosystem Turbo code Step space-filling curve | Provide robustness and security of the EPR data | $512\times 512$ text watermark and different sizes of watermark image | NPSR UACI BER NC | + Robust and secure for medical-image watermarking - Lack of computational complexity test |

Reference | Approaches | Objectives | Cover/Watermark Size | Evaluation Metric | Advantages and Weaknesses |
---|---|---|---|---|---|

[108] | DWT DCT SVD | Robustness against rotation attacks | 512 × 512 Multiple sizes | NC, PSNR | + Robust against various types of attacks including rotation attacks - Few attack analytics - Security was not considered in the scheme |

[109] | DWT DCT Arnold transform | Improve imperceptibility considering robustness | 512 × 512 32 ×32 | ARE SSIM PSNR NC BER | + High PSNR and SSIM values - Lower BER values under various attacks - Limited embedding capacity |

Reference | Architecture | Goal | Embedding Location | Cover/Watermark Size | Evaluation Metric | Advantages and Weaknesses |
---|---|---|---|---|---|---|

[113] | R-CNN | Find the good stength factor used to determine the appropriate location for embedding | DCT-DWT sub-blocks | 512 × 512 4 × 4 | PSNR = 49.10 dB SSIM = 0.99 NC = 1 BER = 0 | + High image quality + Robust against several image-processing attacks, such as JPEG, Gaussian noise, and median filter - Comparison with one existing method is inadequate - Robustness should be investigated in detail - Lower embedding capacity |

[116] | CNNs | Find the optimal region to conceal the watermark | DWT coefficients | MRI scans | correlation coefficient = 1 PSNR = 45.2 dB (without noise scenario) | + High values of correlation coefficient and PSNR - Non-blind - Focus only on imperceptibility |

[117] | ANN | Reduce computational cost of the embedding process | Y channel coefficients | 512 × 512 32 × 32 | PSNR = 39.9 dB SSIM = 0.99 (Lena image) Avg BER = 0.05 Avg embedding time = 0.41 s | + Low computational complexity + Robust against different signal-processing operations - Performance should be studied with multiple watermarks - Geometric attacks must be studied |

[118] | Auto-encoder | Learning codebook images | Vectorised codebook image | 128 × 128 64 × 64 | Avg PSNR = 42.03 dB NC = 0.96 crop($25\%$) | + High security + Imperceptibility and robustness - Non-blind - High computational cost |

Reference | Architecture | Goal | Embedding Location | Cover/Watermark Size | Evaluation Metric | Advantages and Weaknesses |
---|---|---|---|---|---|---|

[17] | Fast R-CNN | Learn to extract the watermark | Block-DCT component | - - | Avg. PSNR = 49.10 dB Extraction time = 1.19 s | + High PSNR values - Focus only on imperceptibility |

[123] | BPNN | Remove noise spikes from the watermarked image | S vector of the host image | 512 × 512 64 × 64 | NC = 0.98 PSNR = 34.78 dB (peppers image, gain = 0.1) | + Good imperceptibility and robustness - High computational complexity - Performance needs to be investigated with multiple watermaks |

[124] | Encoder–Decoder | Recover robust watermark from the host image | DWT coefficients | 512 × 512 64 × 64 | Avg NC = 0.99 Avg PSNR = 47.85 dB Avg SSIM = 0.99 | + Withstand image transformations and geometric distortions - High PSNR values - Security performance should be investigated |

[125] | DNN | Identify changes made by attacks | Block LWT component | 512 × 512 32 × 32 | Avg NC = 0.98 Avg PSNR = 44.11 dB | + Robust against common image-processing attacks + Fast watermark extraction - Needs further improvement in robustness performance |

[23] | R-CNN | Map the relationship between the host image and the watermark image | DCT-SVD sub-blocks | 512 × 512 32 × 32 | NC = 0.9930 (salt and pepper 0.5) | + Outperform the false detection problem + Support multi-type watermark + High security and robustness - Less robust to rotation and cropping - Lack of complexity analysis |

**Table 6.**Summary of learning-based image watermarking methods for embedding and extraction processes.

Reference | Architecture | Goal | Embedding Location | Cover/Watermark Size | Evaluation Metric | Advantages and Weaknesses |
---|---|---|---|---|---|---|

[127] | Image fusion framework | Improve robustness and efficiency | Concatenation of the host image with the watermark image | 128 × 128 32 × 32 | PSNR = 39.72 dB BER = 0 (cropping $20\%$) | + Good performance against typical attacks + Require only the watermarked image for extraction + Applicable in challenging camera processes - Image fusion leads to data loss and slows data processing - Geometric attacks must be investigated |

[128] | Generator–discriminator network | Achieve robustness against rotation and JPEG compression | Luminosity value of the image | 4608 × 3456 8 bit | Avg PSNR = 36.3 dB BER = 0.08 (JPEG Q = 50) | + High robustness against rotation and JPEG compression attacks - Could resist only a limited number of attacks - Execution time must be investigated - Low embedding capacity |

[129] | Backpropagation, Auto-encoders WMnet | Find the robust domain from attacks | Image blocks | 512 × 512 24 bit | PSNR = 40 dB NC = 1 (Rotation 10°) SSIM = 0.98 NC = 1 (Gaussian filtering) | + Optimized robustness and high image quality - Solution for high-definition image is not provided - Low NC values for rotation and cropping |

[130] | Keypoint detection MDResNet | Solve high-definition image watermarking problems | Various scale-invariant regions | 512 × 512 px 512 × 512 bit | PSNR = 43.68 dB SSIM = 0.97 NC = 0.96 BER = 0.01 | + Robust to both common signal operations and geometric attacks + Support high-definition image - High embedding execution time - Robustness to several attacks must be improved |

[131] | CNNs | Learn invisible and robust watermarking | Concatenation of the pre-processed cover and the pre-processed watermark | 128 × 128 8 × 8 | PSNR = 40.58 dB BER = 0.01 (JPEG s = 90) | + Embedding and extraction without restrictions on the cover and the watermark + Good balance between invisibility and robustness - BER values for some geometric attacks are inadequate - Lack of complexity analysis |

[132] | CNNs | Secure robustness of watermarking | Host image blocks | 512 × 512 64 × 64 | PSNR = 39.9 dB (peppers image) NC = 0.98 (Resizing $25\%$, with registration) | + Withstand different types of attacks - Higher detection time - Poor results against rotation |

[133] | ReDMark | Learn new watermarking algorithm in any desired transform domain | Addition of the residual watermark and the host image | 512 × 512 32 × 32 | PSNR = 40.24 dB SSIM = 0.98 ($\alpha =0.6$) BER = 1.6 (JPEG) | + Improved security and robustness especially against JPEG - Withstand only known attacks from training phase - Increasing strength factor decreases PSNR and SSIM |

[134] | Template generation network | Recover watermark from geometric distortions | Predefined locations of the image | 512 × 512 512 × 512 | PSNR = 43.66 dB SSIM = 0.98 BER = 0.12 (Rotation 50°) | + Robust to geometric attacks + High imperceptibility - Large-scale image must be investigated - Focus only on geometric attacks - Lack of execution time analysis |

[135] | Master share framework | Learn zero watermarking | Combination of inherent image features with owner’s watermark sequence | 300 × 300 10 × 10 | PSNR = 33.15 dB (Compression 100) BER = 0.01 NC = 0.98 (Gaussian filtering $\sigma =3$) | + Low computational cost + Robust against several types of attacks - Lack of comparison with existing techniques - Robustness analysis are inadequate especially for synchronization attacks |

[136] | Encoder–Decoder | Resist screen-shooting attacks | Concatenation of the cover and the watermark | 400 × 400 100 bit | PSNR = 34.10 dB SSIM = 0.91 CPP = 6.88 Average bit accuracy > 97 | + High performance against screen-shooting attacks - Lower embedding capacity - Testing with one type of watermark data is inadequate - Image captures are taken with care |

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Boujerfaoui, S.; Riad, R.; Douzi, H.; Ros, F.; Harba, R.
Image Watermarking between Conventional and Learning-Based Techniques: A Literature Review. *Electronics* **2023**, *12*, 74.
https://doi.org/10.3390/electronics12010074

**AMA Style**

Boujerfaoui S, Riad R, Douzi H, Ros F, Harba R.
Image Watermarking between Conventional and Learning-Based Techniques: A Literature Review. *Electronics*. 2023; 12(1):74.
https://doi.org/10.3390/electronics12010074

**Chicago/Turabian Style**

Boujerfaoui, Said, Rabia Riad, Hassan Douzi, Frédéric Ros, and Rachid Harba.
2023. "Image Watermarking between Conventional and Learning-Based Techniques: A Literature Review" *Electronics* 12, no. 1: 74.
https://doi.org/10.3390/electronics12010074