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Target: IT Professionals (Elementary)

Tags: Phishing, Safe Workspace^®, Safe Browsing, NoCloud ™ Anti-Phishing, Object Detection

The cybersecurity landscape constantly changes, and attackers always develop new ways to bypass detection mechanisms. One technique that is becoming more popular is steganography
phishing attacks, especially in object detection. These attacks involve embedding subtle changes into logos or images, preventing them from detection by traditional security measures.
This poses a significant challenge for security measures.

Steganography, derived from the Greek words steganos (covered) and graphia (writing), is the art of concealing information within other data. While historically associated with covert communication, its application has expanded to encompass malicious activities, including phishing attacks. In the context of object detection, steganography involves subtly changing
logos or images to insert imperceptible alterations, thereby deceiving Artificial Intelligence (AI) models while still being visually indistinguishable from human observers.

The primary target of steganography phishing attacks in object detection is to manipulate the confidence levels of AI models. Object detection systems rely on neural networks trained
to accurately identify and classify objects within images. However, these systems can be misled with subtly altered images, leading to misclassification or reduced prediction confidence.

One common approach involves introducing imperceptible noise or hidden images into the logos of legitimate entities. By strategically embedding these alterations, attackers can disrupt
the object detection process without arousing suspicion from human observers. For instance, a seemingly innocuous logo may contain hidden patterns or changes that subtly influence the
AI model’s decision-making process.

The implications of such attacks are profound, particularly in scenarios where object detection plays a critical role in security measures. For example, consider using AI-based systems in
fraud detection, where logos are essential indicators of authenticity. By undermining the reliability of these systems, steganography phishing attacks can facilitate fraudulent activities,
including identity theft and financial fraud. A recently published article has proven that YoloV5, a well-known object detector, can be easily bypassed by using a steganography attack.
As a result, the authors demonstrated that the medical objects were not detected at all, while before the attack, the objects were detected with almost 90% Recall.

Mitigating the threat posed by steganography phishing attacks requires a multi-faceted approach that addresses technical and procedural vulnerabilities. AI models in object detection
must be equipped with robust mechanisms for overcoming subtle alterations introduced through steganographic techniques.

Furthermore, organizations must enhance employee awareness and training programs to recognize other signs of phishing attacks, such as abnormal domain names or top-level domains (TLD).

In conclusion, the rise of steganography phishing attacks presents a formidable challenge to the effectiveness of object detection systems. By exploiting subtle alterations within logos or
images, attackers can bypass traditional detection mechanisms, undermining the integrity of AI-based security measures. Addressing this threat requires a proactive approach that combines technical innovation with user education and robust authentication measures. Only through collective vigilance and adaptation can organizations effectively mitigate the risks posed by steganography phishing attacks in the era of AI-driven cybersecurity.

At BUFFERZONE®, we are highly aware of such attacks, and we will present how to overcome AI attacks with AI in future blogs.

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