Manisha

Detecting AI-synthesized images remains a challenge due to their increasing realism. Traditional methods often fall short in addressing this evolving landscape where testing images can be produced by more powerful generative models or combined with various post-processing operations. To address this, we propose a robust method capitalizing on the distinctive global fingerprints present in real images from physical cameras and fixed patterns in synthesized images generated by generative models. Our approach builds on adapting camera fingerprinting initially designed for source camera identification, leveraging global fingerprints from the mid-frequency band. We rigorously evaluated our methodology through cross-model testing, demonstrating its exceptional generalization capability across different generative models and datasets. Our results, validated on the $DF^{3}$ and CNN-aug datasets with a broader range of generative models, highlight the method’s effectiveness in detecting AI-synthesized images and distinguishing between generative models. Comparisons with state-of-the-art methods show that our approach achieves superior performance, particularly in scenarios involving post-processing operations. This camera fingerprinting approach stands out for its resilience and accuracy, offering a promising solution for image forensics in addressing the challenges posed by sophisticated AI-generated content.

The proliferation of misinformation in the digital age has emerged as a pervasive and pressing challenge, threatening the integrity of information dissemination across online platforms. In response to this growing concern, this survey paper offers a comprehensive analysis of the landscape of misinformation detection methodologies. Our survey delves into the intricacies of model architectures, feature engineering, and data sources, providing insights into the strengths and limitations of each approach. Despite significant advancements in misinformation detection, this survey identifies persistent challenges. The paper accentuates the need for adaptive models that can effectively tackle rapidly evolving events, such as the COVID-19 pandemic. Language adaptability remains another substantial frontier, particularly in the context of low-resource languages like Chinese. Furthermore, it draws attention to the dearth of balanced, multilingual datasets, emphasizing their significance for robust model training and assessment. By addressing emerging challenges and offering a comprehensive view, our paper enriches the understanding of deep learning techniques in misinformation detection.

With the increasing prevalence of digital multimedia content, the need for reliable and accurate source camera identification has become crucial in applications such as digital forensics. While effective techniques exist for identifying the source camera of images, video-based source identification presents unique challenges due to disruptive effects introduced during video processing, such as compression artifacts and pixel misalignment caused by techniques like video coding and stabilization. These effects render existing approaches, which rely on high-frequency camera fingerprints like Photo Response Non-Uniformity (PRNU), inadequate for video-based identification. To address this challenge, we propose a novel approach that builds upon the image-based source identification technique. Leveraging a global stochastic fingerprint residing in the low- and mid-frequency bands, we exploit its resilience to disruptive effects in the high-frequency bands, envisioning its potential for video-based source identification. Through comprehensive evaluation on recent smartphones dataset, we establish new benchmarks for source camera model and individual device identification, surpassing state-of-the-art techniques. While conventional image-based methods struggle in video contexts, our approach unifies image and video source identification through a single framework powered by the novel non-PRNU device-specific fingerprint. This contribution expands the existing body of knowledge in the field of multimedia forensics.

Source-camera identification tools assist image forensics investigators to associate an image with a camera. The Photo Response Non-Uniformity (PRNU) noise pattern caused by sensor imperfections has been proven to be an effective way to identify the source camera. However, the PRNU is susceptible to camera settings, scene details, image processing operations (e.g., simple low-pass filtering or JPEG compression), and counter-forensic attacks. A forensic investigator unaware of malicious counter-forensic attacks or incidental image manipulation is at risk of being misled. The spatial synchronization requirement during the matching of two PRNUs also represents a major limitation of the PRNU. To address the PRNU’s fragility issue, in recent years, deep learning-based data-driven approaches have been developed to identify source-camera models. However, the source information learned by existing deep learning models is not able to distinguish individual cameras of the same model. In light of the vulnerabilities of the PRNU fingerprint and data-driven techniques, in this paper, we bring to light the existence of a new robust data-driven device-specific fingerprint in digital images that is capable of identifying individual cameras of the same model in practical forensic scenarios. We discover that the new device fingerprint is location-independent, stochastic, and globally available, which resolves the spatial synchronization issue. Unlike the PRNU, which resides in the high-frequency band, the new device fingerprint is extracted from the low- and mid-frequency bands, which resolves the fragility issue that the PRNU is unable to contend with. Our experiments on various datasets also demonstrate that the new fingerprint is highly resilient to image manipulations such as rotation, gamma correction, and aggressive JPEG compression.

Source camera identification plays an important role in forensics investigations on images. It is a forensic problem of linking an image in question to the camera used to capture it. Several source identification techniques have been developed in the literature since this may be a facilitating tool that help to trace back the images to the camera device held by the accused in various forensic applications. However, one of the key disadvantages is that the existing techniques fail if the image in question was taken by a new camera that is not used in the training process. Under a real-world forensic scenario, it is not possible to presume that each image being analyzed comes from one of the cameras used to train the source identification system. To address this issue, we propose a data-driven system based on convolutional neural network to identify the source camera device in an open-set scenario. The experimental results on various sets of cameras show that it is possible to leverage the data-driven model as the feature extractor paired with an open-set classifier to trace back the images to the open-set cameras. The results show that the proposed system outperforms the state-of-the-art techniques in identifying the exact device that are never seen before with considerably high accuracy and is resilient to unknown post-processing applied by the social network platforms. Moreover, the experimental results demonstrate the good generalization capability of the proposed system in extracting the source information, making it more suitable for open-set scenarios.