SafeNet: An Assistive Solution to Assess Incoming Threats for Premises
- URL: http://arxiv.org/abs/2002.04405v1
- Date: Mon, 27 Jan 2020 04:33:02 GMT
- Title: SafeNet: An Assistive Solution to Assess Incoming Threats for Premises
- Authors: Shahinur Alam, Md Sultan Mahmud, and Mohammed Yeasin
- Abstract summary: "SafeNet" is an integrated assistive system to generate context-oriented image descriptions to assess incoming threats.
The key functionality of the system includes the detection and identification of human.
To interact with the system, we implemented a dialog enabled interface for creating a personalized profile from face images or videos.
- Score: 0.9558392439655015
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: An assistive solution to assess incoming threats (e.g., robbery, burglary,
gun violence) for homes will enhance the safety of the people with or without
disabilities. This paper presents "SafeNet"- an integrated assistive system to
generate context-oriented image descriptions to assess incoming threats. The
key functionality of the system includes the detection and identification of
human and generating image descriptions from the real-time video streams
obtained from the cameras placed in strategic locations around the house. In
this paper, we focus on developing a robust model called "SafeNet" to generate
image descriptions. To interact with the system, we implemented a dialog
enabled interface for creating a personalized profile from face images or
videos of friends/families. To improve computational efficiency, we apply
change detection to filter out frames that do not have any activity and use
Faster-RCNN to detect the human presence and extract faces using Multitask
Cascaded Convolutional Networks (MTCNN). Subsequently, we apply LBP/FaceNet to
identify a person. SafeNet sends image descriptions to the users with an MMS
containing a person's name if any match found or as "Unknown", scene image,
facial description, and contextual information. SafeNet identifies
friends/families/caregiver versus intruders/unknown with an average F-score
0.97 and generates image descriptions from 10 classes with an average F-measure
0.97.
Related papers
- ID-Guard: A Universal Framework for Combating Facial Manipulation via Breaking Identification [60.73617868629575]
misuse of deep learning-based facial manipulation poses a potential threat to civil rights.
To prevent this fraud at its source, proactive defense technology was proposed to disrupt the manipulation process.
We propose a novel universal framework for combating facial manipulation, called ID-Guard.
arXiv Detail & Related papers (2024-09-20T09:30:08Z) - Privacy-preserving Optics for Enhancing Protection in Face De-identification [60.110274007388135]
We propose a hardware-level face de-identification method to solve this vulnerability.
We also propose an anonymization framework that generates a new face using the privacy-preserving image, face heatmap, and a reference face image from a public dataset as input.
arXiv Detail & Related papers (2024-03-31T19:28:04Z) - Ring-A-Bell! How Reliable are Concept Removal Methods for Diffusion Models? [52.238883592674696]
Ring-A-Bell is a model-agnostic red-teaming tool for T2I diffusion models.
It identifies problematic prompts for diffusion models with the corresponding generation of inappropriate content.
Our results show that Ring-A-Bell, by manipulating safe prompting benchmarks, can transform prompts that were originally regarded as safe to evade existing safety mechanisms.
arXiv Detail & Related papers (2023-10-16T02:11:20Z) - Erasing, Transforming, and Noising Defense Network for Occluded Person
Re-Identification [36.91680117072686]
We propose Erasing, Transforming, and Noising Defense Network (ETNDNet) to solve occluded person re-ID.
In the proposed ETNDNet, we randomly erase the feature map to create an adversarial representation with incomplete information.
Thirdly, we perturb the feature map with random values to address noisy information introduced by obstacles and non-target pedestrians.
arXiv Detail & Related papers (2023-07-14T06:42:21Z) - Attribute-Guided Encryption with Facial Texture Masking [64.77548539959501]
We propose Attribute Guided Encryption with Facial Texture Masking to protect users from unauthorized facial recognition systems.
Our proposed method produces more natural-looking encrypted images than state-of-the-art methods.
arXiv Detail & Related papers (2023-05-22T23:50:43Z) - OPOM: Customized Invisible Cloak towards Face Privacy Protection [58.07786010689529]
We investigate the face privacy protection from a technology standpoint based on a new type of customized cloak.
We propose a new method, named one person one mask (OPOM), to generate person-specific (class-wise) universal masks.
The effectiveness of the proposed method is evaluated on both common and celebrity datasets.
arXiv Detail & Related papers (2022-05-24T11:29:37Z) - Towards a Safety Case for Hardware Fault Tolerance in Convolutional
Neural Networks Using Activation Range Supervision [1.7968112116887602]
Convolutional neural networks (CNNs) have become an established part of numerous safety-critical computer vision applications.
We build a prototypical safety case for CNNs by demonstrating that range supervision represents a highly reliable fault detector.
We explore novel, non-uniform range restriction methods that effectively suppress the probability of silent data corruptions and uncorrectable errors.
arXiv Detail & Related papers (2021-08-16T11:13:55Z) - Privacy-Preserving Video Classification with Convolutional Neural
Networks [8.51142156817993]
We propose a privacy-preserving implementation of single-frame method based video classification with convolutional neural networks.
We evaluate our proposed solution in an application for private human emotion recognition.
arXiv Detail & Related papers (2021-02-06T05:05:31Z) - Face Anti-Spoofing Via Disentangled Representation Learning [90.90512800361742]
Face anti-spoofing is crucial to security of face recognition systems.
We propose a novel perspective of face anti-spoofing that disentangles the liveness features and content features from images.
arXiv Detail & Related papers (2020-08-19T03:54:23Z) - Toward Building Safer Smart Homes for the People with Disabilities [1.0742675209112622]
"SafeAccess" is an end-to-end assistive solution to build a safer smart home by providing situational awareness.
We focus on building a robust model for detecting and recognizing person, generating image descriptions, and designing a prototype for the smart door.
The system notifies users with an MMS containing the name of incoming persons or as "unknown", scene image, facial description, and contextual information.
Our system identifies persons with an F-score 0.97 and recognizes items to generate image description with an average F-score 0.97.
arXiv Detail & Related papers (2020-06-10T15:50:32Z) - CIAGAN: Conditional Identity Anonymization Generative Adversarial
Networks [12.20367903755194]
CIAGAN is a model for image and video anonymization based on conditional generative adversarial networks.
Our model is able to remove the identifying characteristics of faces and bodies while producing high-quality images and videos.
arXiv Detail & Related papers (2020-05-19T15:56:08Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.