Severity Controlled Text-to-Image Generative Model Bias Manipulation

Text-to-image (T2I) generative models are gaining wide popularity, especially in public domains. However, their intrinsic bias and potential malicious manipulations remain under-explored. Charting the susceptibility of T2I models to such manipulation, we first expose the new possibility of a dynamic and computationally efficient exploitation of model bias by targeting the embedded language models. By leveraging mathematical foundations of vector algebra, our technique enables a scalable and convenient control over the severity of output manipulation through model bias. As a by-product, this control also allows a form of precise prompt engineering to generate images which are generally implausible with regular text prompts. We also demonstrate a constructive application of our manipulation for balancing the frequency of generated classes - as in model debiasing. Our technique does not require training and is also framed as a backdoor attack with severity control using semantically-null text triggers in the prompts. With extensive analysis, we present interesting qualitative and quantitative results to expose potential manipulation possibilities for T2I models. Key-words: Text-to-Image Models, Generative Models, Backdoor Attacks, Prompt Engineering, Bias

ARCNet: An Asymmetric Residual Wavelet Column Correction Network for Infrared Image Destriping

Infrared image destriping seeks to restore high-quality content from degraded images. Recent works mainly address this task by leveraging prior knowledge to separate stripe noise from the degraded image. However, constructing a robust decoupling model for that purpose remains challenging, especially when significant similarities exist between the stripe noise and vertical background structure. Addressing that, we introduce Asymmetric Residual wavelet Column correction Network (ARCNet) for image destriping, aiming to consistently preserve spatially precise high-resolution representations. Our neural model leverages a novel downsampler, residual haar discrete wavelet transform (RHDWT), stripe directional prior knowledge and data-driven learning to induce a model with enriched feature representation of stripe noise and background. In our technique, the inverse wavelet transform is replaced by transposed convolution for feature upsampling, which can suppress noise crosstalk and encourage the network to focus on robust image reconstruction. After each sampling, a proposed column non-uniformity correction module (CNCM) is leveraged by our method to enhance column uniformity, spatial correlation, and global self-dependence between each layer component. CNCM can establish structural characteristics of stripe noise and utilize contextual information at long-range dependencies to distinguish stripes with varying intensities and distributions. Extensive experiments on synthetic data, real data, and infrared small target detection tasks show that the proposed method outperforms state-of-the-art single-image destriping methods both visually and quantitatively by a considerable margin. Our code will be made publicly available at \url{https://github.com/xdFai}.

M3BUNet: Mobile Mean Max UNet for Pancreas Segmentation on CT-Scans

Segmenting organs in CT scan images is a necessary process for multiple downstream medical image analysis tasks. Currently, manual CT scan segmentation by radiologists is prevalent, especially for organs like the pancreas, which requires a high level of domain expertise for reliable segmentation due to factors like small organ size, occlusion, and varying shapes. When resorting to automated pancreas segmentation, these factors translate to limited reliable labeled data to train effective segmentation models. Consequently, the performance of contemporary pancreas segmentation models is still not within acceptable ranges. To improve that, we propose M3BUNet, a fusion of MobileNet and U-Net neural networks, equipped with a novel Mean-Max (MM) attention that operates in two stages to gradually segment pancreas CT images from coarse to fine with mask guidance for object detection. This approach empowers the network to surpass segmentation performance achieved by similar network architectures and achieve results that are on par with complex state-of-the-art methods, all while maintaining a low parameter count. Additionally, we introduce external contour segmentation as a preprocessing step for the coarse stage to assist in the segmentation process through image standardization. For the fine segmentation stage, we found that applying a wavelet decomposition filter to create multi-input images enhances pancreas segmentation performance. We extensively evaluate our approach on the widely known NIH pancreas dataset and MSD pancreas dataset. Our approach demonstrates a considerable performance improvement, achieving an average Dice Similarity Coefficient (DSC) value of up to 89.53% and an Intersection Over Union (IOU) score of up to 81.16 for the NIH pancreas dataset, and 88.60% DSC and 79.90% IOU for the MSD Pancreas dataset.

Quantifying Bias in Text-to-Image Generative Models

Bias in text-to-image (T2I) models can propagate unfair social representations and may be used to aggressively market ideas or push controversial agendas. Existing T2I model bias evaluation methods only focus on social biases. We look beyond that and instead propose an evaluation methodology to quantify general biases in T2I generative models, without any preconceived notions. We assess four state-of-the-art T2I models and compare their baseline bias characteristics to their respective variants (two for each), where certain biases have been intentionally induced. We propose three evaluation metrics to assess model biases including: (i) Distribution bias, (ii) Jaccard hallucination and (iii) Generative miss-rate. We conduct two evaluation studies, modelling biases under general, and task-oriented conditions, using a marketing scenario as the domain for the latter. We also quantify social biases to compare our findings to related works. Finally, our methodology is transferred to evaluate captioned-image datasets and measure their bias. Our approach is objective, domain-agnostic and consistently measures different forms of T2I model biases. We have developed a web application and practical implementation of what has been proposed in this work, which is at https://huggingface.co/spaces/JVice/try-before-you-bias. A video series with demonstrations is available at https://www.youtube.com/channel/UCk-0xyUyT0MSd_hkp4jQt1Q

On quantifying and improving realism of images generated with diffusion

Recent advances in diffusion models have led to a quantum leap in the quality of generative visual content. However, quantification of realism of the content is still challenging. Existing evaluation metrics, such as Inception Score and Fr\'echet inception distance, fall short on benchmarking diffusion models due to the versatility of the generated images. Moreover, they are not designed to quantify realism of an individual image. This restricts their application in forensic image analysis, which is becoming increasingly important in the emerging era of generative models. To address that, we first propose a metric, called Image Realism Score (IRS), computed from five statistical measures of a given image. This non-learning based metric not only efficiently quantifies realism of the generated images, it is readily usable as a measure to classify a given image as real or fake. We experimentally establish the model- and data-agnostic nature of the proposed IRS by successfully detecting fake images generated by Stable Diffusion Model (SDM), Dalle2, Midjourney and BigGAN. We further leverage this attribute of our metric to minimize an IRS-augmented generative loss of SDM, and demonstrate a convenient yet considerable quality improvement of the SDM-generated content with our modification. Our efforts have also led to Gen-100 dataset, which provides 1,000 samples for 100 classes generated by four high-quality models. We will release the dataset and code.

Text-image guided Diffusion Model for generating Deepfake celebrity interactions

Deepfake images are fast becoming a serious concern due to their realism. Diffusion models have recently demonstrated highly realistic visual content generation, which makes them an excellent potential tool for Deepfake generation. To curb their exploitation for Deepfakes, it is imperative to first explore the extent to which diffusion models can be used to generate realistic content that is controllable with convenient prompts. This paper devises and explores a novel method in that regard. Our technique alters the popular stable diffusion model to generate a controllable high-quality Deepfake image with text and image prompts. In addition, the original stable model lacks severely in generating quality images that contain multiple persons. The modified diffusion model is able to address this problem, it add input anchor image's latent at the beginning of inferencing rather than Gaussian random latent as input. Hence, we focus on generating forged content for celebrity interactions, which may be used to spread rumors. We also apply Dreambooth to enhance the realism of our fake images. Dreambooth trains the pairing of center words and specific features to produce more refined and personalized output images. Our results show that with the devised scheme, it is possible to create fake visual content with alarming realism, such that the content can serve as believable evidence of meetings between powerful political figures.

PRAT: PRofiling Adversarial aTtacks

Intrinsic susceptibility of deep learning to adversarial examples has led to a plethora of attack techniques with a broad common objective of fooling deep models. However, we find slight compositional differences between the algorithms achieving this objective. These differences leave traces that provide important clues for attacker profiling in real-life scenarios. Inspired by this, we introduce a novel problem of PRofiling Adversarial aTtacks (PRAT). Given an adversarial example, the objective of PRAT is to identify the attack used to generate it. Under this perspective, we can systematically group existing attacks into different families, leading to the sub-problem of attack family identification, which we also study. To enable PRAT analysis, we introduce a large Adversarial Identification Dataset (AID), comprising over 180k adversarial samples generated with 13 popular attacks for image specific/agnostic white/black box setups. We use AID to devise a novel framework for the PRAT objective. Our framework utilizes a Transformer based Global-LOcal Feature (GLOF) module to extract an approximate signature of the adversarial attack, which in turn is used for the identification of the attack. Using AID and our framework, we provide multiple interesting benchmark results for the PRAT problem.

MAiVAR-T: Multimodal Audio-image and Video Action Recognizer using Transformers

In line with the human capacity to perceive the world by simultaneously processing and integrating high-dimensional inputs from multiple modalities like vision and audio, we propose a novel model, MAiVAR-T (Multimodal Audio-Image to Video Action Recognition Transformer). This model employs an intuitive approach for the combination of audio-image and video modalities, with a primary aim to escalate the effectiveness of multimodal human action recognition (MHAR). At the core of MAiVAR-T lies the significance of distilling substantial representations from the audio modality and transmuting these into the image domain. Subsequently, this audio-image depiction is fused with the video modality to formulate a unified representation. This concerted approach strives to exploit the contextual richness inherent in both audio and video modalities, thereby promoting action recognition. In contrast to existing state-of-the-art strategies that focus solely on audio or video modalities, MAiVAR-T demonstrates superior performance. Our extensive empirical evaluations conducted on a benchmark action recognition dataset corroborate the model's remarkable performance. This underscores the potential enhancements derived from integrating audio and video modalities for action recognition purposes.

BAGM: A Backdoor Attack for Manipulating Text-to-Image Generative Models

The rise in popularity of text-to-image generative artificial intelligence (AI) has attracted widespread public interest. At the same time, backdoor attacks are well-known in machine learning literature for their effective manipulation of neural models, which is a growing concern among practitioners. We highlight this threat for generative AI by introducing a Backdoor Attack on text-to-image Generative Models (BAGM). Our attack targets various stages of the text-to-image generative pipeline, modifying the behaviour of the embedded tokenizer and the pre-trained language and visual neural networks. Based on the penetration level, BAGM takes the form of a suite of attacks that are referred to as surface, shallow and deep attacks in this article. We compare the performance of BAGM to recently emerging related methods. We also contribute a set of quantitative metrics for assessing the performance of backdoor attacks on generative AI models in the future. The efficacy of the proposed framework is established by targeting the state-of-the-art stable diffusion pipeline in a digital marketing scenario as the target domain. To that end, we also contribute a Marketable Foods dataset of branded product images. We hope this work contributes towards exposing the contemporary generative AI security challenges and fosters discussions on preemptive efforts for addressing those challenges. Keywords: Generative Artificial Intelligence, Generative Models, Text-to-Image generation, Backdoor Attacks, Trojan, Stable Diffusion.

UnLoc: A Universal Localization Method for Autonomous Vehicles using LiDAR, Radar and/or Camera Input

Localization is a fundamental task in robotics for autonomous navigation. Existing localization methods rely on a single input data modality or train several computational models to process different modalities. This leads to stringent computational requirements and sub-optimal results that fail to capitalize on the complementary information in other data streams. This paper proposes UnLoc, a novel unified neural modeling approach for localization with multi-sensor input in all weather conditions. Our multi-stream network can handle LiDAR, Camera and RADAR inputs for localization on demand, i.e., it can work with one or more input sensors, making it robust to sensor failure. UnLoc uses 3D sparse convolutions and cylindrical partitioning of the space to process LiDAR frames and implements ResNet blocks with a slot attention-based feature filtering module for the Radar and image modalities. We introduce a unique learnable modality encoding scheme to distinguish between the input sensor data. Our method is extensively evaluated on Oxford Radar RobotCar, ApolloSouthBay and Perth-WA datasets. The results ascertain the efficacy of our technique.