A CNN-Based Framework for Enhancing Image Segmentation and Scene Interpretation in Fake Colorized Images: Addressing Challenges in Image Analysis and Understanding

In this section, we present a comprehensive CNN-based framework designed to enhance image segmentation and scene interpretation specifically for detecting fake colorized images. The proposed method integrates multiple advanced techniques to ensure high accuracy, efficiency, and robustness in processing and analyzing images. The framework is divided into several key stages: data preprocessing, model architecture design, training and optimization, and post-processing.

3.1 Data preprocessing

The first step in the proposed method involves preparing the data to be used for training and testing the model. This stage is crucial for ensuring that the model can effectively learn and generalize to new data.

Image Resizing and Normalization

The importance of image normalization as a preprocessing step in deep learning image analysis is to make sure that features are scaled uniformly and the model can converge quickly. Some of the available normalization methods include Min-Max Normalization, Z-score Normalization, and Batch Normalization. All these methods have pros and cons depending on the application [24].

The pixel values are scaled to a fixed range, usually [0, 1] or [-1, 1], using the Min-Max Normalization method, which keeps its relative differences in intensity. On the contrary, Z-score Normalization conforms the data in such a way that it subtracts the mean and divides by the standard deviation, therefore making it robust under varying lighting conditions. Batch Normalization is an advanced form of normalization, which normalizes feature maps within a mini-batch so as to stabilize the training and help with convergence.

Once the input images enter the model for training, the three contrasting normalizations find justification for being considered consistently around these images. The chosen experimental setting is shown to be justified by practical experiments, confirming that it not only works for classification accuracy but also adds a benefit in speed.

In the preprocessing stage, we prepare the input data for effective training and testing of the model.

All images are resized to a uniform size (e.g., 256×256 pixels) to maintain consistency across the dataset. Pixel values are then normalized to a range of 0 to 1, facilitating faster and more stable training.

Each input image I is resized to a fixed dimension $(H, W)$, ensuring uniformity across the dataset. The pixel values are normalized to the range $[0,1]$ to facilitate stable training [25]:

$I_{\text {normalized }}=\frac{I-I_{min }}{I_{max }-I_{min }}$     (1)

where, $I_{\min }$ and $I_{\max }$ are the minimum and maximum pixel values in the image.

Advanced Data Augmentation

To increase the variety of the exercise set and progress the model's overview abilities, advanced data augmentation techniques such as Cutout, Mixup, and CutMix are applied. These techniques help the model become more robust to variations and noise in the data.

In Mixup, two images $\mathrm{I}_1$ and $\mathrm{I}_2$ and their corresponding labels $y_1$ and $y_2$ are combined to create a new training example [26]:

$\begin{aligned} & I_{\text {mix }}=\lambda I_1+(1-\lambda) I_2 \\ & y_{\text {mix }}=\lambda y_1+(1-\lambda) y_2\end{aligned}$     (2)

where, $\lambda$ is a mixing coefficient sampled from a Beta distribution: $\lambda \sim \operatorname{Beta}(\alpha, \alpha)$ with $\alpha>0$.

3.2 Model architecture design

The core of the proposed method is a hybrid CNN-based model designed to effectively capture both local and global features in images. The architecture incorporates several key components:

Convolutional Neural Network (CNN)

The backbone of the model is a deep CNN with multiple convolutional layers, each followed by ReLU activation functions and pooling layers. These layers extract spatial features from the images, allowing the model to learn intricate patterns and textures associated with fake colorization. Each convolutional layer is defined by a filter $W$ and bias $b$, where the output of the convolution operation is given by [27]:

$O_{i j}=\operatorname{ReLU}\left(\sum_{m, n} W_{m n} \cdot I_{(i \mid m)(j \mid n)}+b\right)$     (3)

where, $O_{i j}$ is the output feature map at location $(i, j)$ and ReLU is the Rectified Linear Unit activation function defined as $\operatorname{ReLU}(x)=\max (0, x)$.

Attention Mechanisms

Attention mechanisms enhance deep learning models by dynamically weighting the importance of different features of an image, which enables the network to focus on regions that convey most information. In this particular study, self-attention and spatial attention have been integrated into the approach to enhance fake colorized image detection by refining feature extraction at various levels.

Self-Attention Mechanism: Through the scaled dot-product attention, the model attends to these longer dependencies within the image by examining relationships across different spatial regions: with higher attention weights being assigned to regions of more contextual relevance to the classification at hand. The self-attention calculation involves constructing three sets of matrices: query (Q), key (K), and value (V) matrices. Attention scores are computed using a scaled dot-product of Q and K, which is normalized with a softmax function and used to weight V. The advantage of self-attention is that it gives the model a dynamic way of adjusting the importance of features such that distinguishing between real and fake colorized images is enhanced, especially in those soft cases where color discrepancies are undetectable.

Spatial Attention Mechanism: In contrast, the spatial attention mechanism attempts to find salient regions in an image by making an attention map emphasizing regions of interest. Convolutional transformations are applied onto the feature map to give each pixel an importance score. Spatial attention is greatly beneficial in detecting localized happenings in counterfeit colorized pictures: unnatural edges or sudden color discontinuities that could be missed at a global feature representation. Thus, with spatial attention incorporated, the model gains fine-grained incongruity detection with reasonable computational efficiency.

Implementation and Advantages: By jointly applying self-attention and spatial attention, one obtains a complementary mechanism, where self-attention marries global dependencies while spatial attention fine-tunes local feature representations. Such a bipolar mechanism gives the proposed method leverage over conventional CNNs in making the classification strong against complex scenarios. The experimental evaluation indicates that introducing both attention mechanisms significantly reduces false positives while also improving the model's generalization across different datasets.

To further enhance the model’s focus on relevant areas of the image, attention mechanisms for example Self-Attention or Spatial Attention are integrated. These mechanisms enable the typical to prioritize important regions in the image, which is particularly useful for distinguishing subtle differences between real and fake colorized areas. The attention map $A$ is computed as [28]:

$A=\operatorname{softmax}\left(W_A \cdot F+b_A\right)$     (4)

where, $F$ represents the feature maps from the $\mathrm{CNN}, W \mathrm{~A}$ and $b A$ are learned parameters, and softmax ensures that the attention weights are normalized across the spatial dimensions.

Long Short-Term Memory (LSTM) or Gated Recurrent Units (GRU)

For scenarios involving image sequences (e.g., in video frames), LSTM or GRU layers are added to capture temporal dependencies and enhance the model’s ability to interpret dynamic changes in scenes. For temporal sequence data, a recurrent layer such as LSTM is used, where the hidden state $h t$ at time step $t$ is computed as [29]:

$h_t=\operatorname{LSTM}\left(F_t, h_{t-1}\right)$     (5)

Here, $F_t$ is the input feature at time $t$ and $h_{t-1}$ is the hidden state from the previous time step.

3.3 Training and optimization

Finding an optimal learning rate is crucial for the convergence of a deep-learning model. Bad choices may result in slow convergence or even instability. To help achieve such goals, we implemented a warm-up strategy for the learning rate, i.e., the learning rate starts at a small value and increases linearly to the target learning rate over a few initial epochs, after which it decays. Since warm-up helps to avoid large weight updates during early training, it reduces gradient instability and allows for better generalization. Table 1 summarizes the key hyperparameters used in the training process:

Impact Analysis of Learning Rate Warm-Up: An ablation study was carried out to assess the efficacy of the learning rate warm-up strategy, and three training setups were compared.

• Fixed Learning Rate (No Warm-Up)
• Learning Rate Warm-Up (Proposed Strategy)
• Learning Rate Warm-Up + Cosine Decay [30]

Table 1. Key hyperparameters in the training process

An ablation study was carried out to assess the efficacy of the learning rate warm-up strategy, and three training setups were compared, as shown in Table 2.

Table 2. Ablation study in learning rate warm-up

The results indicate that the model convergence speed is greatly improved by applying a learning rate warm-up such that 28% less epochs will be used for the model to achieve 95% of the final accuracy against a fixed learning rate. Furthermore, cosine decay applied after warm-up improved performance: the method achieved final accuracy 5.1% higher and reduced training loss by 19.7%. These findings prove that the proposed warm-up method stabilizes gradient updates to speed convergence and improve generalization.

Training the model is a critical phase where various optimization techniques are employed to achieve the best possible performance.

Loss function

For binary classification, where the goal is to distinguish between real and fake images, a Focal Loss is used to address class imbalance and improve the model’s sensitivity to hard-to-classify examples. For binary classification, the Focal Loss function is used to address class imbalance [31]:

Focal Loss $\left(p_t\right)=-\alpha_t\left(1-p_t\right)^\gamma \log \left(p_t\right)$

where, $p_t$ is the predicted probability for the true class, $\alpha_t$ is a weighting factor, and $\gamma$ is a focusing parameter that adjusts the importance of correctly classified examples.

Transfer Learning

The proposed model employs transfer learning on a pre-trained network, such as ResNet, Inception, or EfficientNet. The models pre-trained on huge datasets like ImageNet serve a very strong prior, which is fine-tuned on our dataset for improved accuracy and reduced training time.

Dynamic Learning Rate Scheduling

Techniques like Cyclical Learning Rates and Learning Rate Warm-up are employed to dynamically adjust the learning rate during training, ensuring efficient convergence and preventing the model from getting stuck in local minima.

A cyclical learning rate (CLR) schedule is employed to dynamically adjust the learning rate during training. The learning rate $\eta(t)$ at epoch t is computed as [32]:

$\eta(t)=\eta_{\min }+\frac{1}{2}\left(\eta_{\max }-\eta_{\min }\right)\left(1+\cos \left(\frac{t}{T} \pi\right)\right)$

where, $\eta_{\min }$ and $\eta_{\max }$ are the minimum and maximum learning rates, and $T$ is the number of epochs in a cycle.

3.4 Post-processing and evaluation

After training, the model goes through a series of post-processing steps to further refine performance and interpretability.

Ensemble Learning

To ensure maximum robustness in classifications and therefore reduce the number of misclassifications, an ensemble learning method was employed, which combines various models in the interest of overall performance improvement. The great strength of ensemble learning is in combining different architectures while reducing the effect of the biases from individuals' models and enhancing the generalization. Some ensemble strategies such as majority voting, weighted averaging, and stacking were carried out to find a better method for detecting fake colorized images.

We evaluated the following ensemble strategies:

1. Majority Voting - Each model independently predicts the class label, and the final decision is based on the majority vote.

2. Weighted Averaging - The output probabilities of each model are weighted according to individual performance, with higher-performing models contributing more to the final prediction.

3. Stacking - A meta-learner (e.g., logistic regression) is trained for combining predictions from multiple models for optimal classification [33].

The performance of these ensemble methods was compared using key evaluation metrics. Table 3 presents a comparative performance analysis of different ensemble methods.

Table 3. Performance comparison of ensemble methods

The results manifest that ensemble learning provides a boost in performance even when compared to a single model. The weighted average method yielded the best results in terms of accuracy (94.1%); at the same time, it administered low false positive (5.5%) and false negative rates (8.3%); therefore, it is the most efficient combination strategy. Stacking had an accuracy of 93.5%, which was an improvement in its own right but came at a far greater inference time of 55 ms due to the extra meta-learning layer. On a contrary note, majority voting, which is simple enough, ended up having worse performance when contrasted with weighted averaging. These findings ratify that ensemble learning really boosts the robustness of classifications by combining different capacities from each diverse model. The weighted average method adopted strikes a good balance between accuracy as well as efficiency and so is considered suitable for its deployment in real-time applications for the detection of fake images.

Different models are combined in ensemble methods, improving the general performance of better accuracy and reduction of variance. Such a resulting system will be more robust in detection. The final prediction y ̂ is the weighted average of the individual model predictions [34]:

$\hat{y}=\sum_{i=1}^N w_i \hat{y}_i$

where, $N$ is the number of models, $\hat{y}_i$ is the prediction from the $i$-th model, and $w_i$ is its corresponding weight.

Feature Analysis

Techniques such as Grad-CAM or Layer-wise Relevance Propagation allow the illustration of which features and parts of the input the model bases its decisions on. This not only helps to understand the behavior of the model, but also provides insight into where the model may be improved. The Grad-CAM heat-map Lgrad_cam is calculated as [35]:

$L_{\text {grad_cam }}^k=\operatorname{ReLU}\left(\sum_c \alpha_c^k A_c\right)$

where, $\alpha_c^k$ a are the importance weights obtained by global average pooling the gradients of the target class $k$ with respect to the feature map activations $A_c$.

3.5 Deployment in real-world systems

It then prepares the optimized model for deployment in real-world systems by guaranteeing its efficiency and effectiveness in practical scenarios.

Model Compression

Model pruning, quantization, and knowledge distillation are some of the techniques that have been used to reduce model size and computation requirements so that it can be deployed on resource-constrained devices like mobile phones or embedded systems.

System Integration

The model is integrated with a larger system where the real-world conditions of variable light, image quality, and other environmental factors can be tested. This step makes the model robust and reliable in practical scenarios. The flowchart for the fake image detection process is shown in Figure 1.

Efficient deployment of deep learning models in real-world applications requires reducing computational complexity while maintaining high performance. To achieve this, various model compression techniques can be applied, including quantization, pruning, and knowledge distillation, each offering different trade-offs in terms of accuracy, memory footprint, and inference speed.

Comparative Analysis of Model Compression Methods:

• Quantization: The conversion of high-precision (for example, 32-bit floating-point) weights into lower-precision formats (such as 8-bit integers) that reduce the model size and inference latency but could cause minor accuracy degradation.
• Pruning: Removing less important weights or neurons from the network so as to reduce complexity of the model while retaining most of its performance. In its turn, structured pruning can boost inference speed, whereas unstructured pruning minimizes the parameters of the model.
• Knowledge Distillation: A process in which a large, high-performance "teacher" model transfers knowledge to a smaller "student" model, maintaining performance but with reduced computational requirements.

46b85a74-4c9e-4e9e-acf6-06f907dde565.png

Figure 1. Flowchart outline for fake image detection process

Table 4. Key simulation parameters for the proposed method

To quantify the effect of these compression techniques on the proposed model, a comparative study was performed. Results suggest that 8-bit quantization cuts the model down to 25% of its original size, with only 1.9% loss in accuracy. Pruning at 40% sparsity yielded a 55% reduction in model parameters while keeping 98% of the original accuracy intact. Knowledge distillation gave the most balanced results, achieving a 60% reduction in model size for 99% of the original accuracy at a much-reduced computational cost.

From these results, the deployment will use a combination of quantization and knowledge distillation, as this yields the best compromise between efficiency and performance. Improved future work may allow some adaptive compression whereby the compression is dynamically tuned by the deployment constraints.

3.6 Simulation parameters

In order to simulate and put into practice the proposed approach, the key parameters involved may be divided into three kinds of categories: modeling parameters, optimization parameters, and the settings of the neural network. Table 4 gives a list of some suggested parameters.

To have a more accurate analysis of the proposed method, in this section, we compared additional experiments. In the following, the results of an error and confusion-based analysis are presented.

5.1 Error analysis

A more detailed analysis of error trend reveals that subtle color variations are still a challenge for fake colorized image detection. In certain cases, the gradual hue shift, slight saturation inconsistency, or nearly-identical adjacent color distributions have presented hurdles to the models all around. The proposed approach has demonstrated better performance in identifying these kinds of variations when compared with PanColorGAN and CCAR-TAR; however, distinguishing between artificially colorized images and naturally occurring low-contrast areas continues to be a challenge. For example, we presented a test case involving an actual black-and-white historical image that was artificially colorized; our proposed method managed to find texture inconsistencies but misclassified it because of the highly plausible yet artificial-looking color gradients. On the contrary, CCAR-TAR obtained a higher false-positive rate (17.6%), frequently labeling genuine colorized images as fake because of its lesser inclination toward natural color transitions. On the other hand, PanColorGAN, while being more competent on a global scale in judging color inconsistencies, had a lower success rate with fine pixel variations, leading to a false-negatives rate of 22.3% where subtle shading and lighting changes were the major indicators for forgery.

Table 8. Performance comparison of models on images with subtle color variations

f94c365e-660d-4472-98e1-7c1f19dc09a8.png

Figure 6. The error analysis of the proposed method compared with the others

To compare model performances in these cases, another evaluation was conducted on a subset of controlled color variations in 500 images. The results show that:

The proposed method was able to identify images with slight color difference with an accuracy of 91.4% and a false negative of 8.6%.

On the same dataset, PanColorGAN had a lower accuracy of 84.7%, with a false negative of 22.3%.

CCAR-TAR gave the lowest performance with accuracy at 79.1% and a false positive of 17.6%, which meant it misclassified often.

An error performance analysis was carried out versus other state-of-the-art models, namely PanColorGAN and CCAR-TAR. The two categorically selected categories that impose a very challenging task are chosen as "Complex Textures" and "Subtle Color Variations." For this purpose, the author has selected two categories, mainly "Complex Textures".

This comparison, as summarized in Table 8, highlights the strengths and weaknesses of each method when applied to images with subtle color variations. The Proposed Method demonstrates the best overall accuracy, with the lowest false positive and false negative rates.

Regarding "Complex Textures," this proposed method yielded better performances with much lower errors relative to PanColorGAN and CCAR-TAR. In the category where there is usually a repetition of intricate patterns or the depiction of detailed surface areas, models with an overreliance on simple features really encounter problems. With a preponderance toward colorization and generative aspects of image generation, PanColorGAN fared worse than expected, incorrectly distinguishing almost all the slight differences of a certain class. Similarly, CCAR-TAR was strong in other areas but failed to capture the minute details of complex textures and hence gave more errors, see Figure 6.

In contrast, the architecture of the proposed method, which embeds multi-scale feature extraction and enhanced convolutional layers, was effective in grasping the fine-grained details that are necessary for distinguishing between similar textures. This capability is crucial in applications where texture plays a pivotal role, such as in material classification or scene analysis.

For "Subtle Color Variations," again the proposed method outperformed the competing models. Minor color variations in hue, saturation, or brightness will easily lead to misclassifications if the color information is not well utilized by the model. PanColorGAN generated quite plausible colorizations; still, it was wrong with a higher error rate on this category since the PanColorGAN is generative, not designed for accurate classification.

In addition, CCAR-TAR also had certain limitations in the form of recognition accuracy. This is probably because dependence on classic convolution filters barred the model from differentiating minor differences of color, hence prone to errors. However, in this proposed system, special-colored sensitive layers and attention mechanisms for advanced colored processing have been incorporated that enhances its capabilities in detecting and correctly placing an image with minor color variation. This is indicative of the method's robustness and flexibility in scenarios that require subtle color discrimination.

It can be observed from the error distribution chart of comparative analysis that, under all categories considered in the comparison, the proposed method tends to perform much better than PanColorGAN and CCAR-TAR. Lower errors while handling complex textures and minute changes in color indicate a stronger ability in processing detailed visual information.

This superior performance is attributed to the novelty in integrating multi-scale feature extraction, advanced color-processing layers, and an attention mechanism within the proposed model design. These enhancements enable the model to capture a greater range of visual features and thus cope with the challenges provided by complex textures and discreet color variations.

5.2 Confusion matrix analysis

Here, a confusion matrix-based analysis for the proposed method, PanColorGAN, and CCAR-TAR was performed to have a deep view about the classification performance, emphasizing how well each model distinguishes between classes, see Figure 7.

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Figure 7. The confusion matrix analysis of the proposed method compared with the others