EagleEye

Pros of sherlock

• More actively maintained with frequent updates and contributions
• Supports a larger number of websites and platforms (350+)
• Better documentation and user guides

Cons of sherlock

• Focuses solely on username searches, lacking image recognition capabilities
• May produce more false positives due to its broad search approach

Code Comparison

EagleEye (Python):

def get_social_media_profiles(self):
    for website in self.websites:
        try:
            self.profiles.append(website.check_username(self.username))
        except Exception as e:
            print(f"Error checking {website.name}: {str(e)}")

sherlock (Python):

def sherlock(username, site_data, timeout=60):
    results = {}
    for social_network, net_info in site_data.items():
        results[social_network] = {"url_main": net_info.get("urlMain")}
        url = net_info["url"].format(username=username)
        results[social_network]["url_user"] = url
        results[social_network]["status"] = "Claimed"
        # ... (additional code for request handling and status checking)

Both projects aim to find social media profiles, but sherlock offers a more comprehensive approach with a larger database of supported websites. EagleEye, while less actively maintained, provides additional functionality for image recognition and facial analysis, which sherlock lacks. sherlock's code structure allows for easier expansion and maintenance of the supported platforms list.

Pros of social-analyzer

• Supports a wider range of social media platforms (300+)
• Offers both CLI and API interfaces for flexibility
• Provides more detailed output, including profile pictures and additional metadata

Cons of social-analyzer

• More complex setup and dependencies
• May be slower due to the larger number of platforms it searches
• Less focused on facial recognition compared to EagleEye

Code Comparison

EagleEye:

def get_social_media_profiles(self):
    self.facebook_profile = self.find_facebook_profile()
    self.linkedin_profile = self.find_linkedin_profile()
    self.twitter_profile = self.find_twitter_profile()

social-analyzer:

def run_as_object(self, username, silent=False, output="good", filter_output="", profiles=""):
    # ... (initialization code)
    for site in sites:
        if site["selected"] == "true":
            # ... (site-specific search logic)
    return {"detected": detected, "unknown": unknown, "failed": failed, "profiles": profiles}

The code snippets highlight the different approaches:

• EagleEye focuses on specific platforms with dedicated methods
• social-analyzer uses a more generic approach to handle multiple platforms

Both tools aim to find social media profiles, but social-analyzer offers a more comprehensive search across a larger number of platforms, while EagleEye appears to be more specialized for a few major social networks.

Pros of Maigret

• Supports a wider range of social networks and websites (over 2000)
• More actively maintained with regular updates
• Includes additional features like report generation and proxy support

Cons of Maigret

• May have a steeper learning curve due to more complex functionality
• Potentially slower execution time when searching across numerous platforms
• Requires more dependencies and setup compared to EagleEye

Code Comparison

EagleEye (Python):

def get_social_media_profiles(self):
    for website in self.websites:
        self.driver.get(website)
        if self.driver.title == "Page not found":
            continue
        # Process and extract profile information

Maigret (Python):

async def search(self, username, site_dict, query_notify):
    tasks = []
    for site in site_dict:
        check_func = site.check_func or self.standard_check
        task = asyncio.ensure_future(check_func(site, username, query_notify))
        tasks.append(task)
    await asyncio.gather(*tasks)

The code snippets show that Maigret uses asynchronous programming for potentially faster execution when searching multiple sites simultaneously, while EagleEye uses a more straightforward sequential approach.

Pros of holehe

• Focuses specifically on email address reconnaissance
• Supports a larger number of websites and services (over 120)
• Faster execution due to asynchronous requests

Cons of holehe

• Limited to email-based searches only
• Does not include image recognition or facial search capabilities
• Less comprehensive profile information gathering

Code Comparison

holehe:

async def instagram(email):
    try:
        async with aiohttp.ClientSession() as session:
            async with session.get("https://www.instagram.com/accounts/web_create_ajax/attempt/", timeout=10) as response:
                if response.status == 200:
                    return {"exists": True, "email_recovery": None}
    except Exception:
        return {"exists": False, "email_recovery": None}

EagleEye:

def instagram(self):
    try:
        response = requests.get('https://www.instagram.com/' + self.username)
        if response.status_code == 200:
            return True
    except:
        return False

The code snippets show that holehe focuses on email-based checks, while EagleEye performs username-based searches. holehe uses asynchronous requests with aiohttp, potentially offering better performance for multiple checks.

Pros of GHunt

• More focused on Google account information retrieval
• Actively maintained with recent updates
• Supports command-line interface for easier automation

Cons of GHunt

• Limited to Google accounts, less versatile for general OSINT
• Requires more setup and dependencies
• May be more complex for beginners

Code Comparison

EagleEye:

def get_profile_pic(self):
    if self.profile_picture:
        return self.profile_picture
    else:
        return None

GHunt:

def get_profile_picture(gaiaID):
    url = f"https://lh3.googleusercontent.com/a-/{gaiaID}"
    response = requests.head(url)
    if response.status_code == 200:
        return url
    return None

Both projects aim to gather information from online sources, but they differ in their focus and implementation. EagleEye is designed for broader social media reconnaissance, while GHunt specializes in Google account information retrieval. EagleEye may be easier for beginners to use, but GHunt offers more in-depth analysis of Google-related data. The code comparison shows that both projects have similar functions for retrieving profile pictures, but GHunt's implementation is more specific to Google's infrastructure.

Pros of snoop

• More active development with frequent updates
• Supports a wider range of social networks and platforms
• Offers a user-friendly command-line interface

Cons of snoop

• Primarily focused on Russian-language platforms and users
• Less emphasis on facial recognition capabilities
• Requires more manual input and configuration

Code comparison

EagleEye:

def get_social_media_profiles(self):
    for website in self.websites:
        if website.search():
            print("[+] Profile found on {}".format(website.name))
        else:
            print("[-] Profile not found on {}".format(website.name))

snoop:

def start(self):
    for module in self.modules:
        try:
            module.main(self.username)
        except Exception as e:
            self.logger.error(f"Error in module {module.__name__}: {str(e)}")

Both projects aim to gather information about individuals across various online platforms. EagleEye focuses more on facial recognition and image analysis, while snoop emphasizes username searches across a broader range of social networks. EagleEye is more specialized but potentially more powerful for visual identification, whereas snoop offers a more comprehensive search across text-based profiles and platforms.