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Effective Detection of Generated Images Using Frequency Transform
Hyoungwon Seo, Dongsu Kim, Seoyoen Oh, Jisang Lee, Haneol Jang
http://doi.org/10.5626/JOK.2025.52.4.350
In today's digital era, advanced image generation techniques have produced counterfeit images that are nearly indistinguishable from real ones, thereby undermining the trustworthiness of digital information. Conventional machine learning and deep learning methods have shown limitations when confronting these evolving generative algorithms. This study introduces a novel approach that can analyze characteristics of generated images in the frequency domain. Specifically, we independently applied the Fast Fourier Transform (FFT) and the Discrete Cosine Transform (DCT) to evaluate the effectiveness of each method for detecting generated images. Experimental results revealed that the FFT-based model improved the test accuracy by approximately 12.8%, while the DCT-based model demonstrated a performance enhancement of about 22.2%. These findings confirm that a frequency domain approach outperforms traditional spatial domain-based detection techniques. It is expected to make a substantial contribution to enhancing image reliability in digital forensics.
Parallel Algorithms for Finding Consensus of Circular Strings
The consensus problem is finding a representative string, called a consensus, of a given set S of k strings. Circular strings are different from linear strings in that the last symbol precedes the first symbol. Given a set S of circular strings of length n over an alphabet ∑ , we first present an O(｜∑｜nlogn) time parallel algorithm for finding a consensus of S minimizing both radius and distance sum when k=3 using O(n) threads. Then we present an O(｜∑｜n²logn) time parallel algorithm for finding a consensus of S minimizing distance sum when k=4 using O(n) threads. Finally, we compare execution times of our algorithms implemented using CUDA with corresponding sequential algorithms.
Motor Imagery EEG Classification Method using EMD and FFT
David Lee, Hee-Jae Lee, Sang-Goog Lee
Electroencephalogram (EEG)-based brain-computer interfaces (BCI) can be used for a number of purposes in a variety of industries, such as to replace body parts like hands and feet or to improve user convenience. In this paper, we propose a method to decompose and extract motor imagery EEG signal using Empirical Mode Decomposition (EMD) and Fast Fourier Transforms (FFT). The EEG signal classification consists of the following three steps. First, during signal decomposition, the EMD is used to generate Intrinsic Mode Functions (IMFs) from the EEG signal. Then during feature extraction, the power spectral density (PSD) is used to identify the frequency band of the IMFs generated. The FFT is used to extract the features for motor imagery from an IMF that includes mu rhythm. Finally, during classification, the Support Vector Machine (SVM) is used to classify the features of the motor imagery EEG signal. 10-fold cross-validation was then used to estimate the generalization capability of the given classifier., and the results show that the proposed method has an accuracy of 84.50% which is higher than that of other methods.
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