In the realm of digital media, the advent of AI-generated synthetic images
has introduced significant challenges in distinguishing between real and
fabricated visual content. These images, often indistinguishable from authentic
ones, pose a threat to the credibility of digital media, with potential
implications for disinformation and fraud. Our research addresses this
challenge by employing machine learning techniques to discern between
AI-generated and genuine images. Central to our approach is the CIFAKE dataset,
a comprehensive collection of images labeled as "Real" and "Fake". We refine
and adapt advanced deep learning architectures like ResNet, VGGNet, and
DenseNet, utilizing transfer learning to enhance their precision in identifying
synthetic images. We also compare these with a baseline model comprising a
vanilla Support Vector Machine (SVM) and a custom Convolutional Neural Network
(CNN). The experimental results were significant, demonstrating that our
optimized deep learning models outperform traditional methods, with DenseNet
achieving an accuracy of 97.74%. Our application study contributes by applying
and optimizing these advanced models for synthetic image detection, conducting
a comparative analysis using various metrics, and demonstrating their superior
capability in identifying AI-generated images over traditional machine learning
techniques. This research not only advances the field of digital media
integrity but also sets a foundation for future explorations into the ethical
and technical dimensions of AI-generated content in digital media.

利用机器学习技术来区分 AI 生成的合成图像和真实图像，通过优化深度学习模型和对比实验结果表明，深度学习模型可以更准确地识别合成图像，进一步推动数字媒体可信度及 AI 生成内容的伦理和技术探索。

利用机器学习识别人工智能生成的合成图像

Harnessing Machine Learning for Discerning AI-Generated Synthetic Images

Recent technological advances in synthetic data have enabled the generation
of images with such high quality that human beings cannot tell the difference
between real-life photographs and Artificial Intelligence (AI) generated
images. Given the critical necessity of data reliability and authentication,
this article proposes to enhance our ability to recognise AI-generated images
through computer vision. Initially, a synthetic dataset is generated that
mirrors the ten classes of the already available CIFAR-10 dataset with latent
diffusion which provides a contrasting set of images for comparison to real
photographs. The model is capable of generating complex visual attributes, such
as photorealistic reflections in water. The two sets of data present as a
binary classification problem with regard to whether the photograph is real or
generated by AI. This study then proposes the use of a Convolutional Neural
Network (CNN) to classify the images into two categories; Real or Fake.
Following hyperparameter tuning and the training of 36 individual network
topologies, the optimal approach could correctly classify the images with
92.98% accuracy. Finally, this study implements explainable AI via Gradient
Class Activation Mapping to explore which features within the images are useful
for classification. Interpretation reveals interesting concepts within the
image, in particular, noting that the actual entity itself does not hold useful
information for classification; instead, the model focuses on small visual
imperfections in the background of the images. The complete dataset engineered
for this study, referred to as the CIFAKE dataset, is made publicly available
to the research community for future work.

使用合成数据，基于计算机视觉，通过卷积神经网络和可解释的 AI 技术实现对人眼难以区分真实照片和 AI 生成的图片的分类，最终建立的 CIFAKE 数据集对于未来的研究工作提供了有价值的资源。