Recent AI tools can subtly manipulate images, eroding users’ trust in the authenticity of images they see on their displays. Current image authentication methods either detect artifacts that may result from manipulations or attach hashes of images as metadata for users to verify. The efficacy of the first approach is rapidly deteriorating with the continuous improvements in AI tools, leading to missing many serious manipulations. Hashes become invalid once images are subjected to any processing, such as re-sizing and transcoding. This makes the second approach impractical as most platforms, e.g., Facebook and X, perform several legitimate operations on images. Further, most platforms remove the metadata attached to images. We propose RDIAS, a robust and practical image authentication system. RDIAS securely embeds representative fingerprints into images without damaging their visual quality. We design these fingerprints to robustly detect malicious manipulations, e.g., adding/removing objects, while tolerating legitimate operations, e.g., image resizing and transcoding. Rigorous evaluation of RDIAS with diverse image datasets and realistic manipulations conducted by human subjects utilizing AI tools shows its high accuracy and efficiency. For example, RDIAS detects DeepFake manipulations that change facial features/expressions with an accuracy of 99%. The results also show that RDIAS preserves image quality and verifies authenticity in real time.

Most current watermarking methods offer low and fixed capacity, which means they can only embed small-size watermarks into images. Additionally, they are typically robust to only a small subset of the known image transformations (aka distortions) that occur during the processing, transmission, and storage of images. These shortcomings limit their adoption in many practical multimedia applications. We propose FlexMark, a robust and adaptive watermarking method for images, which achieves a better capacity-robustness trade-off than current methods and can easily be used for different applications. FlexMark categorizes and models the fundamental aspects of various image transformations, enabling it to achieve high accuracy in the presence of many practical transformations. FlexMark introduces new ideas to further improve the performance, including double-embedding of the input message, employing self-attention layers to identify the most suitable regions in the image to embed the watermark bits, and utilization of a discriminator to improve the visual quality of watermarked images. In addition, FlexMark offers a parameter, α, to enable users to control the trade-off between robustness and capacity to meet the requirements of different applications. We implement FlexMark and assess its performance using datasets commonly used in this domain. Our results show that FlexMark is robust against a wide range of image transformations, including ones that were never seen during its training, which shows its generality and practicality. Our results also show that FlexMark substantially outperforms the closest methods in the literature in terms of capacity and robustness.

Face-based authentication systems are among the most commonly used biometric systems, because of the ease of capturing face images at a distance and in non-intrusive way. These systems are, however, susceptible to various presentation attacks, including printed faces, artificial masks, and makeup attacks. In this paper, we propose a novel solution to address makeup attacks, which are the hardest to detect in such systems because makeup can substantially alter the facial features of a person, including making them appear older/younger by adding/hiding wrinkles, modifying the shape of eyebrows, beard, and moustache, and changing the color of lips and cheeks. In our solution, we design a generative adversarial network for removing the makeup from face images while retaining their essential facial features and then compare the face images before and after removing makeup. We collect a large dataset of various types of makeup, especially malicious makeup that can be used to break into remote unattended security systems. This dataset is quite different from existing makeup datasets that mostly focus on cosmetic aspects. We conduct an extensive experimental study to evaluate our method and compare it against the state-of-the art using standard objective metrics commonly used in biometric systems as well as subjective metrics collected through a user study. Our results show that the proposed solution produces high accuracy and substantially outperforms the closest works in the literature.

Due to recent technological advances in capturing and processing devices, hyperspectral imaging is becoming available for many commercial and military applications such as remote sensing, surveillance, and forest fire detection. Hyperspectral cameras provide rich information, as they capture each pixel along many frequency bands in the spectrum. The large volume of hyperspectral images as well as their high dimensionality make transmitting them over limited-bandwidth channels a challenge. To address this challenge, we present a method to prioritize the transmission of various components of hyperspectral data based on the application needs, the level of details required, and available bandwidth. This is unlike current works that mostly assume offline processing and the availability of all data beforehand. Our method jointly and optimally selects the spectral bands and their qualities to maximize the utility of the transmitted data. It also enables progressive transmission of hyperspectral data, in which approximate results are obtained with small amount of data and can be refined with additional data. This is a desirable feature for large-scale hyperspectral imaging applications. We have implemented the proposed method and compared it against the state-of-the-art in the literature using hyperspectral imaging datasets. Our experimental results show that the proposed method achieves high accuracy, transmits a small fraction of the hyperspectral data, and significantly outperforms the state-of-the-art; up to 35% improvements in accuracy was achieved.