Article_2:Image Forgery Detection: A Survey of Recent Deep-Learning Approaches_Part2

ادامه پست معرفی مقاله Image Forgery Detection: A Survey of Recent Deep-Learning Approaches

۲.۳. مزایا و محدودیت‌ها

مزایا:
 مقایسه کامل و جامع بین روش‌های کلاسیک و یادگیری عمیق.
 بررسی نقاط قوت و ضعف هر روش در انواع مختلف جعل.

محدودیت‌ها:
 داده‌های آزمایشی این مقاله به اندازه مقالات دیگر جامع نیست.

مقاله به روش‌های ترکیبی یادگیری ماشین و یادگیری عمیق کمتر پرداخته است.

  

2.1. Research Objective

This paper presents a comprehensive review of traditional and modern image forgery detection techniques, comparing machine learning methods with deep learning-based approaches. The goal is to evaluate the effectiveness of classical approaches against advanced forgery methods such as DeepFake and whether deep learning completely replaces traditional methods.

2.2. Methodology

Traditional Forgery Detection Methods:

• Machine learning models such as SVM, K-NN, Random Forest, and Naïve Bayes
• Texture analysis techniques including Local Binary Patterns (LBP) and Histogram of Oriented Gradients (HOG)
• Frequency domain analysis using Fourier Transform (FFT) and Discrete Wavelet Transform (DWT)

Comparison with Deep Learning-Based Methods:

• Analysis of CNN-based architectures and GAN-Detection models
• Evaluation of ResNet, EfficientNet, and XceptionNet in forgery detection

2.3. Strengths & Limitations

Strengths:

Comprehensive comparison between traditional ML and deep learning models.

 Evaluates strengths and weaknesses of various methods.

Limitations:

 The dataset used in experiments is not as extensive as other research studies.

 The paper does not explore hybrid methods combining ML and deep learning.