Synthetic Media

5 automated security scanners

Defensive Content Marking

Purpose: The Defensive Content Marking Scanner is designed to detect tamper-evident markings and tracking mechanisms within domain and company-related content. This tool aims to ensure the integrity and authenticity of information, preventing unauthorized alterations by identifying digital signatures, watermarks, cryptographic seals, embedded tracking scripts, mentions of malware or trojan activities, and more.

What It Detects:

Tamper-Evident Markings: Identifies digital signatures or hashes embedded in web pages, detects watermarks or other visual indicators of tampering protection, and verifies the presence of cryptographic seals on content.
Tracking Mechanisms: Locates embedded tracking scripts (e.g., Google Analytics, custom trackers), identifies beaconing mechanisms that report back to external servers, and detects hidden fields in forms that track user interactions.
Vulnerability Indicators: Searches for known CVE identifiers within the content, looks for mentions of malware, ransomware, or trojan activities, and identifies command and control (C2) server references.
Exposure Indicators: Detects phrases indicating data exposure, leaks, or breaches, locates mentions of unauthorized access or data dumps, and identifies indicators of compromised systems or services.
Security Policy References: Finds links to security policies, incident response plans, or compliance statements, verifies the presence of contact information for reporting vulnerabilities, and detects references to third-party security audits or certifications.

Inputs Required:

domain (string): Primary domain to analyze (e.g., acme.com)
company_name (string): Company name for statement searching (e.g., “Acme Corporation”)

Business Impact: This scanner is crucial for organizations aiming to protect their sensitive information from unauthorized tampering and exposure. By identifying potential vulnerabilities and tracking mechanisms, it helps in maintaining the security posture of corporate digital assets, ensuring compliance with regulatory standards, and safeguarding against cyber threats.

Risk Levels:

Critical: The critical risk level pertains to conditions that directly lead to severe data breaches or significant system compromises, such as unauthorized access to highly sensitive information or systems without proper authorization.
High: High-risk findings include vulnerabilities that can be exploited with minimal effort, potentially leading to substantial damage if not mitigated promptly. This includes exposure of critical business and personal data through improper configurations or inadequate security measures.
Medium: Medium risk findings involve potential threats where the impact could be significant but requires more advanced techniques or larger volumes of data for exploitation. These include moderate vulnerabilities that might lead to partial data loss or limited unauthorized access scenarios.
Low: Low risk findings are generally informational in nature, such as minor security policy non-compliance or usage of outdated protocols, which do not pose immediate threats but should be addressed for overall improvement and compliance.
Info: Informational findings provide insights into the baseline configuration and practices without significant impact on security posture. These include basic security measures that are generally compliant with industry standards but could be enhanced for better protection.

Example Findings:

A web page contains a digital signature embedded in its metadata, which is indicative of tamper-evident protections being implemented to prevent unauthorized modifications.
An internal document mentions an outdated encryption method used for sensitive data storage, indicating a potential vulnerability that needs immediate attention to align with current security standards and best practices.

AI-Generated Text Detection

Purpose: The AI-Generated Text Detection Scanner is designed to identify and distinguish between human-written text and content generated by artificial intelligence. It analyzes language patterns, coherence, and stylistic elements that deviate from typical human writing to detect synthetic media threats such as deepfakes, fake news, and automated phishing attempts.

What It Detects:

Language Coherence Patterns: The scanner detects repetitive or unnatural sentence structures, identifies overly formal or informal language inconsistent with standard human writing, and flags excessive use of jargon or technical terms without appropriate context.
Stylistic Anomalies: It analyzes punctuation and capitalization inconsistencies, detects unusual word choice or phrasing that deviates from natural language patterns, and identifies the lack of contractions or idiomatic expressions commonly used in human writing.
Automated Campaign Indicators: The scanner looks for repetitive content across multiple sources with slight variations, detects identical or near-identical text blocks across different domains, and flags the use of generic templates without personalization.
Lack of Contextual Understanding: It identifies sentences that lack context or fail to connect logically, detects irrelevant information or tangential topics inserted into the text, and flags overgeneralizations or broad statements lacking specific details.
Statistical Anomalies: The scanner analyzes word frequency and distribution for deviations from typical human norms, detects unusual sentence length variations, and identifies patterns of repetition that are unlikely in natural writing.

Inputs Required:

domain (string): Primary domain to analyze (e.g., acme.com) - This input is essential for the scanner to gather content from relevant pages on the specified domain.
company_name (string): Company name for statement searching (e.g., “Acme Corporation”) - This helps in identifying specific statements or mentions within the company’s online presence that can be analyzed for AI-generated patterns.

Business Impact: Identifying and mitigating synthetic media threats such as deepfakes, fake news, and automated phishing attempts is crucial for maintaining trust and security in digital communications. Detecting AI-generated content helps organizations safeguard their reputation and prevent malicious use of synthetic media in various scenarios that could compromise national security or public trust.

Risk Levels:

Critical: The scanner identifies patterns indicative of AI-generated content, stylistic anomalies that are highly unlikely in human writing, and significant deviations from typical language usage without contextual justification.
High: The scanner detects repetitive or unnatural sentence structures, excessive use of jargon or technical terms without context, and lack of personalization in content templates.
Medium: The scanner flags punctuation and capitalization inconsistencies, unusual word choice or phrasing that deviates from natural language patterns, and minor deviations from standard language usage.
Low: The scanner identifies isolated instances of generic template use or slight deviations from typical human writing without clear AI-generated indicators.
Info: The scanner detects minimal deviations from standard language usage in a contextually appropriate manner, with no significant deviations indicating AI generation.

Example Findings:

A news article that contains numerous grammatical errors and uses highly repetitive sentence structures, suggesting possible AI generation.
An official company blog post that consistently employs overly formal language and technical jargon without clear relevance to the topic or audience engagement, indicative of automated content creation tools.

Deepfake Detection

Purpose: The Deepfake Detection Scanner is designed to identify manipulated media by detecting anomalies in video, voice synthesis, and image content. Its primary purpose is to prevent the spread of misinformation and protect organizational reputation.

What It Detects:

Video Manipulation Indicators:
- Unnatural movements or inconsistencies in facial expressions.
- Frame rate irregularities indicating spliced footage.
- Audio-video synchronization issues.
- Repetitive patterns or glitches that suggest deepfake generation.
- Unusual lighting conditions or shadows inconsistent with natural scenes.
Voice Synthesis Anomalies:
- Unnatural speech patterns or pitch variations.
- Inconsistencies in voice modulation and intonation.
- Presence of background noise or artifacts not typical of human speech.
- Unnatural pauses or stutters that suggest synthetic generation.
- Discrepancies between lip movements and spoken words.
Image Manipulation Patterns:
- Pixelation or blurring indicating altered regions.
- Inconsistencies in lighting, shadows, or reflections.
- Presence of cloned elements or repeated textures.
- Unnatural color gradients or hue shifts.
- Objects or features that do not align with the rest of the image.
Metadata Anomalies:
- Missing or inconsistent metadata fields.
- Unusual file creation or modification dates.
- Absence of camera-specific data indicating professional equipment use.
- Discrepancies in EXIF data between different media files.
- Inconsistencies in encoding parameters that suggest post-processing.
Deepfake Detection Algorithms:
- Deep learning artifacts such as unnatural skin tones or textures.
- Inconsistencies in facial landmarks and feature points.
- Presence of neural network-generated patterns not found in natural media.
- Anomalies in audio spectrograms indicating synthetic voice generation.
- Discrepancies between visual and auditory elements suggesting separate synthesis.

Inputs Required:

domain (string): Primary domain to analyze (e.g., acme.com)
company_name (string): Company name for statement searching (e.g., “Acme Corporation”)

Business Impact: This scanner is crucial for organizations dealing with digital media, as it helps in identifying and mitigating the risks associated with manipulated content that could lead to significant damage to reputation and trust.

Risk Levels:

Critical: Findings indicating clear evidence of deepfake manipulation that poses immediate risk to organizational integrity.
High: Significant anomalies detected which are highly indicative of manipulation, requiring urgent attention to prevent further dissemination of misinformation.
Medium: Notable deviations from expected patterns in media content that might suggest tampering but do not pose an immediate threat.
Low: Minor inconsistencies or anomalies that may require monitoring for potential changes but do not indicate a high risk at present.
Info: Minimal findings with little to no impact on organizational security, primarily informative for awareness and future trend tracking.

Example Findings:

“Detected unnatural movements in video sample.” - Indicates a possible deepfake video that requires further investigation.
“Found inconsistencies in voice modulation.” - Suggests the presence of synthetic audio content that needs to be verified and potentially addressed.

Manipulated Content Analysis

Purpose: The Manipulated Content Analysis Scanner is designed to identify and detect selective editing, context removal, and splicing in media content. This tool is crucial for ensuring the authenticity and integrity of information by identifying manipulated images, videos, and other multimedia that may mislead the public or spread misinformation.

What It Detects:

Selective Editing Patterns: The scanner detects cropped or altered sections using metadata analysis, checks for inconsistencies in lighting, shadows, and backgrounds, verifies alignment of elements across frames or layers, identifies unnatural transitions between segments, and flags mismatched audio-video synchronization.
Context Removal Indicators: It searches for missing context clues that would normally be present in unaltered content, identifies abrupt changes in narrative flow without logical explanation, checks for removed timestamps, watermarks, or other identifying marks, detects inconsistencies in background elements across frames, and flags sudden shifts in camera angles or perspectives.
Splicing Patterns: The scanner analyzes frame-by-frame to detect spliced content using visual and audio cues, checks for differences in compression artifacts between segments, verifies consistency of lighting, shadows, and reflections across splices, detects unnatural transitions between video clips, and flags inconsistencies in audio levels or background noise.
Metadata Anomalies: The scanner examines metadata for signs of manipulation (e.g., altered timestamps, inconsistent camera settings), checks for missing or inconsistent EXIF data, verifies consistency of file creation and modification dates, detects discrepancies between reported and actual file sizes, and flags unusual encoding parameters that suggest post-production editing.
Content Consistency Checks: The scanner performs cross-referencing with known authentic versions of the content, analyzes visual elements for signs of duplication or repetition, checks for inconsistencies in text, logos, or other branding elements, detects unnatural scaling or resizing of objects within the media, and flags discrepancies in color grading or saturation levels.

Inputs Required:

domain (string): Primary domain to analyze (e.g., acme.com). This is crucial for identifying potential sources of manipulated content across the organization’s digital assets.
company_name (string): Company name for statement searching (e.g., “Acme Corporation”). This helps in focusing the analysis on relevant domains and media content related to the company, enhancing the effectiveness of the detection process.

Business Impact: Ensuring the authenticity and integrity of information is paramount in maintaining trust and credibility in digital communications. Manipulated content can lead to significant risks such as spreading misinformation, undermining public trust, and potentially causing harm or legal repercussions for organizations involved in the production or dissemination of manipulated media.

Risk Levels:

Critical: Conditions that directly indicate severe manipulation with high potential impact on decision-making processes or widespread deception (e.g., significant alterations detected without clear context).
High: Conditions indicating substantial manipulation likely to mislead stakeholders, such as abrupt narrative changes in video content not supported by contextual information.
Medium: Conditions suggesting moderate levels of manipulation that might require further investigation for verification but do not necessarily impact critical decision-making (e.g., minor inconsistencies in lighting across frames).
Low: Informal findings or conditions with minimal practical impact on trust, authenticity, and integrity assessments (e.g., minor metadata discrepancies that could be clarified through additional context).
Info: Conditions primarily informative for awareness raising but not directly impacting the core detection of manipulation (e.g., routine adjustments in digital media typical in post-production processes).

Example Findings:

“Detected cropped sections in image.jpg, suggesting potential selective editing.”
“Missing context clues in video.mp4 indicate possible splicing or removal of critical information.”

Media Authentication

Purpose: The Media Authentication Scanner is designed to safeguard digital media by ensuring its provenance and integrity. It identifies embedded watermarks, verifies content authenticity, checks for known vulnerabilities in media handling software, detects malicious activities through threat intelligence indicators, and scans for malware signatures in media files. This tool is crucial for protecting against manipulated or fake media that could mislead audiences.

What It Detects:

Digital Watermark Presence: Identifies the presence of visible or invisible digital watermarks within images and videos using specific patterns to detect common watermarking techniques.
Content Provenance Verification: Verifies the origin and authenticity of media content by cross-referencing with trusted sources, checking for metadata inconsistencies that may indicate tampering.
Known Exploited Vulnerabilities: Scans for known vulnerabilities in media handling software using CISA KEV, identifying if systems or tools used to create or distribute media are vulnerable.
Threat Intelligence Indicators: Utilizes threat intelligence feeds from Shodan, VirusTotal, and AbuseIPDB to detect malicious activities related to media content, looking for patterns indicative of compromised systems or malicious actors.
Malicious Content Detection: Scans for malware signatures in media files using the VirusTotal API, identifying potential threats embedded within media content that could exploit vulnerabilities.

Inputs Required:

domain (string): Primary domain to analyze (e.g., acme.com)
company_name (string): Company name for statement searching (e.g., “Acme Corporation”)

Business Impact: This scanner is essential for organizations handling digital media, ensuring that the content they disseminate or use is authentic and not tampered with. It helps in maintaining trust among stakeholders by guaranteeing the integrity of multimedia assets.

Risk Levels:

Critical: Conditions where there are significant concerns about the authenticity or provenance of media content, potentially leading to severe consequences if undetected (e.g., legal disputes, reputational damage).
High: Situations where there is a high probability that media has been manipulated or tampered with, affecting trust and integrity but not necessarily posing immediate risks (e.g., unauthorized alterations in video footage).
Medium: Issues requiring attention to verify the authenticity of media content, which could lead to potential vulnerabilities if left unaddressed (e.g., concerns about metadata consistency).
Low: Minor issues that do not significantly impact trust or integrity but are still worth addressing for continuous improvement (e.g., minor discrepancies in watermark patterns).
Info: Informative findings that provide insights into the media’s handling and distribution history, useful for auditing and compliance purposes.

If specific risk levels are not detailed in the README, they can be inferred based on the scanner’s purpose and potential impact.

Example Findings:

Digital Watermark Presence: A JPEG image contains a visible watermark that could be used to track its distribution or authenticity.
Content Provenance Verification: Metadata for a video file indicates inconsistent sources, suggesting possible tampering with the original content.