Recent advancements in artificial intelligence (AI) have revolutionized the field of image generation. This has concurrently escalated social problems and concerns related to AI image generation, underscoring the necessity for an effective AI-generated-image detection method. Therefore, numerous methods for detecting AI-generated images have been developed, but there remains a need for research comparing the effectiveness of and visualizing these detection methods. In this study, we classify AI-generated-image detection methods by the image features they use and compare their generalization performance in detecting AI-generated images of different types. We selected five AI-generated-image detection methods for performance evaluation and selected vision transformer as an additional method for comparison. We use two types of training datasets, i.e., ProGAN and latent diffusion; combine existing AI-generated-image test datasets into a diverse test dataset; and divide them into three types of generative models, i.e., generative adversarial network (GAN), diffusion, and transformer, to evaluate the comprehensive performance of the detection methods. We also analyze their detection performance on images with data augmentation, considering scenarios that make it difficult to detect AI-generated images. Grad-CAM and t-SNE are used to visualize the detection area and data distribution of each detection method. As a result, we determine that artifact-feature-based detection performs well on GAN and real images, whereas image-encoder-feature-based detection performs well on diffusion and transformer images. In summary, our research analyzes the comparative detection performance of various AI-generated-image detection methods, identifies their limitations, and suggests directions for further research.

With the advancement of facial recognition technology, concerns over facial privacy breaches owing to data leaks and external attacks have been escalating. Existing de-identification methods face challenges with compatibility with facial recognition models and difficulties in verifying de-identified images. To address these issues, this study introduces a novel framework that combines face verification-enabled de-identification techniques with face-swapping methods, tailored for video surveillance environments. This framework employs StyleGAN, Pixel2Style2Pixel (PSP), HopSkipJumpAttack (HSJA), and FaceNet512 to achieve face verification-capable de-identification, and uses the dlib library for face swapping. Experimental results demonstrate that this method maintains high face recognition performance (98.37%) across various facial recognition models while achieving effective de-identification. Additionally, human tests have validated its sufficient de-identification capabilities, and image quality assessments have shown its excellence across various metrics. Moreover, real-time de-identification feasibility was evaluated using Nvidia Jetson AGX Xavier, achieving a processing speed of up to 9.68 fps. These results mark a significant advancement in demonstrating the practicality of high-quality de-identification techniques and facial privacy protection in the field of video surveillance.

Deep neural networks (DNN) are applied in various fields because they afford good performance. However, they seem vulnerable to adversarial examples. Therefore, studies are actively investigating adversarial defenses to improve the robustness of DNNs as well as adversarial attacks to generate more intensive adversarial perturbations. Most existing attack methods excessively modify images by generating overly large distortions or adding perturbations to regions with little impact on the DNN. As a result, the local smoothness of images is not maintained, and changes can be easily detected using a steganalysis-based detector. In this study, we propose a contour attack method to evade steganalysis-based detection. This method extracts the contour region from an image and adds perturbations only to applicable regions, thus maintaining the local smoothness. It can solve the problems mentioned above and effectively evade steganalysis-based detectors. Our experimental results demonstrate that the local smoothness is better than that in the case of original attack methods and that the detection evasion rate for a steganalysis detector is up to 19.9% higher. Further, structural similarity (SSIM) measurements and image comparisons demonstrate the improved imperceptibility of perturbations. In particular, the SSIM values for images subjected to the contour attack are close to 1.0.

Although deep neural networks (DNNs) are applied in various fields owing to their remarkable performance, recent studies have indicated that DNN models are vulnerable to backdoor attacks. Backdoored images were generated by adding a backdoor trigger in original training images, which activated the backdoor attack. However, most of the previously used attack methods are noticeable, not natural to the human eye, and easily detected by certain defense methods. Accordingly, we propose an image-synthesis-based backdoor attack, which is a novel approach to avoid this type of attack. To overcome the aforementioned limitations, we set a conditional facial region such as the hair, eyes, or mouth as a trigger and modified that region using an image synthesis technique that replaced the region of original image with the region of target image. Consequently, we achieved an attack success rate of up to 88.37% using 20% of the synthesized backdoored images injected in the training dataset while maintaining the model accuracy for clean images. Moreover, we analyzed the advantages of the proposed approach through image transformation, visualization of activation regions for DNN models, and human tests. In addition to its applicability in both label flipping and clean-label attack scenarios, the proposed method can be utilized as an attack approach to threaten security in the face classification task.

Split learning is a neural network training approach that can overcome the limitations of traditional deep neural networks in edge artificial intelligence environments. It offers the advantage of privacy protection because it transmits intermediate features that are calculated via the client-side model and the client does not need to send the original input data to the server. However, concerns remain regarding data privacy leakage because an attacker can still attempt model inversion attacks based on the intermediate features. We introduce several shortcomings of existing defense techniques for such attacks and present a new defense approach called TrapMI. The proposed method can induce an attacker to generate a class-specific target image that appears different from the original image when inverting the input image. We analyze the performance through quantitative and qualitative evaluations. Furthermore, the AutoGenerator is proposed to overcome the problem whereby the client cannot perform modulation that requires the target image because the class of the input image is unknown during this phase. De-identified images are automatically modulated in the inference phase using this approach. The proposed method was evaluated on two datasets, three classification models, and three split points. Its resistance was measured using a deeper and stronger inverse model than those in previous studies. Overall, the proposed method ensures data privacy protection at a significantly higher level while maintaining a similar task performance to that of existing defense technologies.