Cyber Security Images: Keeping You Protected?
By Tom Seest
At BestCybersecurityNews, we help young learners and seniors learn more about cybersecurity.
Cybersecurity is the discipline responsible for safeguarding computer systems, back-end networks, user applications, and their associated data from external threats or intrusion.
Cyber security is of utmost importance for businesses, governments, and medical and financial organizations that store large amounts of sensitive data on computers or other devices. Furthermore, anyone wanting to safeguard themselves online should consider investing in cyber security measures.
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Cybersecurity professionals rely on facial recognition as one of the most critical technologies. It can be employed to track individuals, identify suspicious characters in photos, and prevent identity theft.
Facial recognition technology utilizes a computer to recognize and match people in photos against databases of similar faces. To be successful, these algorithms must be able to comprehend the geometry of human faces, including distinguishing landmarks like eyes, brows, nose, mouth, chin, and jaw.
Face recognition systems typically learn by training on a collection of images and videos of people. The system then learns to read the geometry of a human face by analyzing different features such as lighting, skin tone, and eye position.
Many governments are exploring facial recognition technology as a means of identifying missing individuals and preventing crimes. Law enforcement can also utilize it by comparing photographs of suspects against names in a database of convicted criminals.
It is essential to realize that these systems are not perfect. Even minor adjustments, like changing a camera angle or getting a haircut, can produce inaccurate results and lead to the misidentification of innocent individuals who may have committed crimes.
Governments are relying on these systems to identify suspected criminals, but they should never be used without adequate legal safeguards and extensive public discussion. Laws can protect individuals while discouraging corporate abuse of the systems.
In addition to tracking criminals, these systems can also be employed to identify missing individuals, such as children or seniors. In China, teachers use tablets to scan students’ faces in order to determine if they’re present in class.
Religious groups and retailers employ these systems to monitor patrons, identifying suspicious characters and shoplifters. Airline agents also rely on these systems to confirm passengers’ identities and prevent fraudulence.
As facial recognition technology is increasingly employed for criminal investigations, privacy and civil rights concerns have grown. San Francisco (California) became the first city to ban its use for police surveillance in May 2019, while Somerville (Massachusetts) followed suit shortly afterward.
The debate over facial recognition in cybersecurity is complex, so it’s essential that we educate ourselves on both its advantages and potential drawbacks before making our decisions. While we should not underestimate its power, we must also be cognizant of its potential uses and implications for individual liberties.
Image steganography is a coding technique employed by cybercriminals to conceal an offensive payload within an unassuming digital image. This evasion technique has become common in ransomware attacks and other attacks against enterprise networks.
Steganographic techniques involve altering some pixels in an image file to make them invisible to those unfamiliar with the algorithm behind it. This is often accomplished using an LSB (Least Significant Bit) technique, which alters the last bit of pixel data so that viewers of the original image cannot detect changes made.
LSB (Latent Semiconductor) steganography works by altering the last bit of three data bytes in a pixel, which is based on RGB colors – red, green, and blue. This technique is one of the easiest and most popular image steganography methods available today.
Another steganography technique is the transform domain method, which involves altering a pixel’s information. This more intricate and sophisticated method requires specialized tools for implementation.
Steganography can be useful for concealing a significant amount of information within an image, but it also has several drawbacks. First and foremost, these techniques are not foolproof; anyone with the appropriate tools and knowledge could easily detect them.
Second, steganography methods are vulnerable to various attacks, such as spoofing and data mining. Furthermore, these techniques may not be ideal for large images since they become cumbersome and slow to process.
Finally, steganography can be difficult to detect with standard anti-malware and APT tools. This is because image steganography circumvents standard security systems, allowing hackers to upload malicious files over a network.
Thankfully, the cybersecurity industry is developing new steganography techniques that are resistant to these attacks. Deep learning-based methods, in particular, have proven particularly successful at this, being able to detect and decipher hidden messages in images while being resilient enough to withstand standard image processing operations like compression or resizing.
Image encryption is an important research area in cyber security images, as digital images are frequently used for daily network communication, and it’s essential to protect people’s privacy. Thus, developing an image encryption algorithm that can secure the contents of a digital image and decrypt it so you can restore the original contents when needed is paramount.
To address this problem, we utilize an HHP scrambling method to increase randomness in matrix elements and strengthen our algorithm’s security. Furthermore, region energy matching embedding is employed for reduced damage to the carrier image. Ultimately, a visually secure cipher image is generated by embedding a noise-like secret image into this noise-like carrier image.
Figure 1 illustrates this, showing the original Lena grayscale image encrypted and decrypted using a proposed encryption algorithm with excellent security. When the encryption key is changed, the recovered decrypted image differs significantly from before due to a lack of valid information – an indication that this algorithm is sensitive to key input.
The pixel distributions of both encrypted and carrier images are highly similar. The histogram of the encrypted image has a low variance and standard deviation, while its y-coordinate value is smaller than that of the plain image. Conversely, the histogram of the carrier image has higher variance and standard deviation but has a slightly larger y-coordinate value than its counterpart.
This leads to a high correlation between the secret and carrier images since the secret image pixels are distributed widely, and the correlation between adjacent secret image pixels is strong. However, the proposed encryption algorithm offers high security; it can withstand chosen-plaintext attack (CPA) as well as known-plaintext attack (KPA).
To test the effectiveness of our proposed encryption algorithm, we conducted a central data cropping attack and an edge data cropping attack on an encrypted image. The results show that both attacks significantly reduce quality after decryption; however, neither has any significant effect. Therefore, our proposed encryption can withstand these attacks with improved results.
Image analysis is an integral component of cyber security, as it allows users to detect any tampering or manipulation with an image. Techniques such as inspecting an image’s metadata or checking its file format for changes can help detect such incidents.
Computers are indispensable for many tasks, yet human analysts remain necessary for certain types of analysis. Medical doctors and law enforcement officers may require computer-based imaging technology in order to detect certain diseases or crimes.
One way to do this is by analyzing the image’s metadata, which can reveal information about who created it and what device or software was used. Another approach would be to examine the image itself and detect any signs of manipulation using forensic tools.
However, these methods can be time-consuming and expensive. That is why the National Geospatial-Intelligence Agency (NGA) is looking to automate some aspects of image analysis, freeing up analysts for more complex tasks.
Researchers have devised various image-based techniques to address these problems. These include scene feature-based forensics, camera feature-based forensics, and compression-based forensics.
Furthermore, machine learning techniques can be employed to perform image analysis on these data to enhance the efficiency of resulting models. For instance, neural networks can be trained on images in order to recognize code authors and detect malware.
Some of these technologies can be implemented in real-time, which is essential for applications requiring rapid responses. This is especially critical for physical security measures like surveillance and object tracking as well as digital ones like biometric authentication or watermarking.
One intriguing approach involves converting raw packets into images and using these images to train an analysis model. This approach has several advantages, such as the capacity for detecting anomalies in images and guaranteeing that the generated images match those from the original packets.
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