  Deepfakes represent one of the toughest challenges in the world of
Cybersecurity and Digital Forensics, especially considering the high-quality
results obtained with recent generative AI-based solutions. Almost all
generative models leave unique traces in synthetic data that, if analyzed and
identified in detail, can be exploited to improve the generalization
limitations of existing deepfake detectors. In this paper we analyzed deepfake
images in the frequency domain generated by both GAN and Diffusion Model
engines, examining in detail the underlying statistical distribution of
Discrete Cosine Transform (DCT) coefficients. Recognizing that not all
coefficients contribute equally to image detection, we hypothesize the
existence of a unique ``discriminative fingerprint", embedded in specific
combinations of coefficients. To identify them, Machine Learning classifiers
were trained on various combinations of coefficients. In addition, the
Explainable AI (XAI) LIME algorithm was used to search for intrinsic
discriminative combinations of coefficients. Finally, we performed a robustness
test to analyze the persistence of traces by applying JPEG compression. The
experimental results reveal the existence of traces left by the generative
models that are more discriminative and persistent at JPEG attacks. Code and
dataset are available at https://github.com/opontorno/dcts_analysis_deepfakes.
