Research on Energy Efficiency Management of Forklift Based on Improved YOLOv5 Algorithm

As an important tool for loading, unloading, and distributing palletized goods, forklifts are widely used in different links of industrial production process. However, due to the rapid increase in the types and quantities of goods, item statistics have become a major bottleneck in production. Based on machine vision, the paper proposes a method to count the amount of goods loaded and unloaded within the working time limit to analyze the efficiency of the forklift. The proposed method includes the data preprocessing section and the object detection section. In the data preprocessing section, through operations such as framing and clustering the collected video data and using the improved image hash algorithm to remove similar images, a new dataset of forklift goods was built. In the object detection section, the attention mechanism and the replacement network layer were used to improve the performance of YOLOv5. The experimented results showed that, compared with the original YOLOv5 model, the improved model is lighter in size and faster in detection speed without loss of detection precision, which could also meet the requirements for real-time statistics on the operation efficiency of forklifts.

Integrity and Authenticity of Digital Images by Digital Forensic Analysis of Metadata

Abstract—During various court hearings, the thesis that every authentic digital file has precise metadata of its creation date was questioned.In this way, the problem was raised which indicates, if the metadata of a digital file (Image) whose label records the date of creation by the recording device of a digital image file are accurate and reliable.For this reason, during the forensic analysis carried out in this work, a record of the metadata of five (05) digital image files from known sources is shown and where their characteristics have been detailed, in addition a record of the metadata of the images used that were later manipulated with image editing software with which metadata comparisons were made to show the labels that suffered modifications in their content.Finally, the obtaining of HASH code with the SHA - 256 algorithm is shown, for digital assurance, of the edited and original files whose comparison allows observing the changes in the content at a binary level. Keywords—Crime; Cybercrime; Digital Image; HASH; Metadata

An Innovative Approach to Solve Healthcare Issues Using Big Data Image Analytics

The increasing adoption of transmission of medical images through internet in healthcare has led to several security threats to patient medical information. Permitting quiet data to be in peril may prompt hopeless harm, ethically and truly to the patient. Accordingly, it is important to take measures to forestall illicit access and altering of clinical pictures. This requests reception of security components to guarantee three fundamental security administrations – classification, content-based legitimacy, and trustworthiness of clinical pictures traded in telemedicine applications. Right now, inside created symmetric key cryptographic capacities are utilized. Pictorial model-based perceptual image hash is used to provide content-based authentication for malicious tampering detection and localization. The presentation of the projected algorithm has been evaluated using performance metrics such as PSNR, normalized correlation, entropy, and histogram analysis, and the simulation results show that the security services have been achieved effectively.

An Image Similarity Evaluation in Rainfall Forecasting Model

The Global Satellite Mapping of Precipitation or GSMaP data which is used to display the rainfall data was used to analyze and create the rainfall forecasting model. This work is the evaluation of this rainfall forecasting model which is the short-term forecast. The GSMaP forecasting data were matched with the GSMaP history data and calculate their similarity values by applying the original image matching method. The modification of Rainfall Forecasting Model and its evaluation that applied the original image instead of the image hash improve the accuracy of rainfall forecasted results.

A Practical Blockchain Framework using Image Hashing for Image Authentication

Blockchain is a relatively new technology that can be seen as a decentralised database. Blockchain systems heavily rely on cryptographic hash functions to store their data, which makes it difficult to tamper with any data stored in the system. A topic that was researched along with blockchain is image authentication. Image authentication focuses on investigating and maintaining the integrity of images. As a blockchain system can be useful for maintaining data integrity, image authentication has the potential to be enhanced by blockchain. There are many techniques that can be used to authenticate images; the technique investigated by this work is image hashing. Image hashing is a technique used to calculate how similar two different images are. This is done by converting the images into hashes and then comparing them using a distance formula. To investigate the topic, an experiment involving a simulated blockchain was created. The blockchain acted as a database for images. This blockchain was made up of devices which contained their own unique image hashing algorithms. The blockchain was tested by creating modified copies of the images contained in the database, and then submitting them to the blockchain to see if it will return the original image. Through this experiment it was discovered that it is plausible to create an image authentication system using blockchain and image hashing. However, the design proposed by this work requires refinement, as it appears to struggle in some situations. This work shows that blockchain can be a suitable approach for authenticating images, particularly via image hashing. Other observations include that using multiple image hash algorithms at the same time can increase performance in some cases, as well as that each type of test done to the blockchain has its own unique pattern to its data.

A Secure and Robust Image Hashing Scheme Using Gaussian Pyramids

Image hash is an alternative to cryptographic hash functions for checking integrity of digital images. Compared to cryptographic hash functions, an image hash or a Perceptual Hash Function (PHF) is resilient to content preserving distortions and sensitive to malicious tampering. In this paper, a robust and secure image hashing technique using a Gaussian pyramid is proposed. A Gaussian pyramid decomposes an image into different resolution levels which can be utilized to obtain robust and compact hash features. These stable features have been utilized in the proposed work to construct a secure and robust image hash. The proposed scheme uses Laplacian of Gaussian (LOG) and disk filters to filter the low-resolution Gaussian decomposed image. The filtered images are then subtracted and their difference is used as a hash. To make the hash secure, a key is introduced before feature extraction, thus making the entire feature space random. The proposed hashing scheme has been evaluated through a number of experiments involving cases of non-malicious distortions and malicious tampering. Experimental results reveal that the proposed hashing scheme is robust against non-malicious distortions and is sensitive to detect minute malicious tampering. Moreover, False Positive Probability (FPP) and False Negative Probability (FNP) results demonstrate the effectiveness of the proposed scheme when compared to state-of-the-art image hashing algorithms proposed in the literature.