8 research outputs found
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.Comment: 10 pages, 5 figure
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.Comment: 8 pages,8 figures, DICT
A Cloud-based Healthcare Framework for Security and Patients’ Data Privacy Using Wireless Body Area Networks
AbstractThe recent developments in remote healthcare systems have witnessed significant interests from IT industry (Microsoft, Google, VMware etc) that provide ubiquitous and easily deployable healthcare systems. These systems provide a platform to share medical information, applications, and infrastructure in a ubiquitous and fully automated manner. Communication security and patients’ data privacy are the aspects that would increase the confidence of users in such remote healthcare systems. This paper presents a secure cloud-based mobile healthcare framework using wireless body area networks (WBANs). The research work presented here is twofold: first, it attempts to secure the inter-sensor communication by multi-biometric based key generation scheme in WBANs; and secondly, the electronic medical records (EMRs) are securely stored in the hospital community cloud and privacy of the patients’ data is preserved. The evaluation and analysis shows that the proposed multi-biometric based mechanism provides significant security measures due to its highly efficient key generation mechanism
Reinforced Meta-path Selection for Recommendation on Heterogeneous Information Networks
Heterogeneous Information Networks (HINs) capture complex relations among
entities of various kinds and have been used extensively to improve the
effectiveness of various data mining tasks, such as in recommender systems.
Many existing HIN-based recommendation algorithms utilize hand-crafted
meta-paths to extract semantic information from the networks. These algorithms
rely on extensive domain knowledge with which the best set of meta-paths can be
selected. For applications where the HINs are highly complex with numerous node
and link types, the approach of hand-crafting a meta-path set is too tedious
and error-prone. To tackle this problem, we propose the Reinforcement
learning-based Meta-path Selection (RMS) framework to select effective
meta-paths and to incorporate them into existing meta-path-based recommenders.
To identify high-quality meta-paths, RMS trains a reinforcement learning (RL)
based policy network(agent), which gets rewards from the performance on the
downstream recommendation tasks. We design a HIN-based recommendation model,
HRec, that effectively uses the meta-path information. We further integrate
HRec with RMS and derive our recommendation solution, RMS-HRec, that
automatically utilizes the effective meta-paths. Experiments on real datasets
show that our algorithm can significantly improve the performance of
recommendation models by capturing important meta-paths automatically